Performing studies of consumer behavior determined using electronically-captured consumer location data

ABSTRACT

In embodiments, methods and systems for performing studies of consumer behavior are provided. The consumer behavior may have been determined using electronically-captured consumer location data for multiple consumers. The gathered data may be analyzed to determine behavior patterns or other characteristics of the multiple consumers. Further, inferences or predictions about consumers may be derived based on the characteristics. The inferences and predictions may be the basis of consumer analytics supplied to a business or other entity as results of a study of the consumer behavior.

RELATED APPLICATIONS

This Application claims the benefit under 35 U.S.C. §120 of U.S.application Ser. No. 12/910,372, entitled “PERFORMING STUDIES OFCONSUMER BEHAVIOR DETERMINED USING ELECTRONICALLY-CAPTURED CONSUMERLOCATION DATA” filed on Oct. 22, 2010, which is herein incorporated byreference in its entirety. Application Ser. No. 12/910,372 claimspriority under 35 U.S.C. §119(e) to U.S. Provisional Application Ser.No. 61/254,328, entitled “METHOD AND SYSTEM FOR CONSUMER BEHAVIORANALYSIS USING ELECTRONICALLY CAPTURED CONSUMER LOCATION DATA” filed onOct. 23, 2009, which is herein incorporated by reference in itsentirety. Application Ser. No. 12/910,372 claims priority under 35U.S.C. §119(e) to U.S. Provisional Application Ser. No. 61/309,751,entitled “METHOD AND SYSTEM FOR CONSUMER BEHAVIOR ANALYSIS USINGELECTRONICALLY CAPTURED CONSUMER LOCATION DATA” filed on Mar. 2, 2010,which is herein incorporated by reference in its entirety.

BACKGROUND

1. Technical Field

The invention relates generally to analyzing consumer characteristicsand more specifically to making inferences and predictions aboutconsumer behavior based on automatically collected consumer locationdata.

2. Discussion of Related Art

Businesses can often benefit from knowledge about the behavior of theircustomers or prospective customers. For example, a business may offercertain products or undertake a marketing strategy based on its beliefsregarding who its customers are. If these beliefs are inaccurate,though, the business' efforts may be misdirected and the business mayfail to maintain old customers or attract new customers.

Efforts have been previously made at collecting information aboutconsumers who may be customers and prospective customers of a business.In some such techniques, a researcher may ask consumers about theiridentities, preferences or behaviors using direct questioning. Thesequestions may be designed to solicit particular information aboutconsumers, such as regions in which a business' customers live, asocioeconomic grouping of consumers, how often the consumers shop at thebusiness, factors influencing purchasing decisions, and their consumingpreferences. Written or oral questionnaires, one-on-one interviews,brief point-of-sale questions at the business, focus groups, andtelephone or online surveys are examples of ways in which informationabout consumers can be collected using direct questioning.

This same information may be voluntarily provided by consumers when theconsumers register for a service. This may be the case when consumersare registering for discount programs or for services offeredcommercially by the business. Thus, when a consumer subscribes toservices offered by the business, direct questions may solicitinformation that may be used to acquire information about the individualconsumer and for the general class of that business' consumers. Theacquired information may then be analyzed to determine informationuseful to the business.

SUMMARY

In one embodiment, there is provided a method comprising operating atleast one programmed processor to carry out a set of acts, where the atleast one programmed processor is programmed with executableinstructions identifying the set of acts. The set of acts comprises, foreach of a plurality of consumers, receiving a plurality of pieces oflocation data, each piece of location data identifying a physicallocation through which a consumer passed, and analyzing the locationdata for each of the plurality of consumers to determine at least onebehavioral characteristic of the consumer. The set of acts furthercomprises predicting, based on the at least one behavioralcharacteristic of the plurality of consumers, a characteristic ofoperation of a retail facility when operated at each of one or morelocations.

In another embodiment, there is provided a method comprising operatingat least one programmed processor to carry out a set of acts, where theat least one programmed processor is programmed with executableinstructions identifying the set of acts. The set of acts comprisesobtaining a plurality of pieces of location data for a consumer, eachpiece of location data identifying a location for the consumer andidentifying, based at least in part on the plurality of pieces oflocation data for the consumer, whether the consumer likely viewed anadvertisement by determining whether a location of the consumerindicated by the plurality of pieces of location data matches a locationof the advertisement. The set of acts further comprises, when theconsumer is determined to be likely to have viewed the advertisement,determining an effectiveness of the advertisement on the consumer.

In a further embodiment, there is provided a method comprising operatingat least one programmed processor to carry out a set of acts, where theat least one programmed processor is programmed with executableinstructions identifying the set of acts. The set of acts comprises, foreach consumer of a plurality of consumers, obtaining location data for acurrent location of the consumer and comparing the location data to atleast one location for at least one known setting to determine a settingcorresponding to the location data, and inferring, for an organizationassociated with a setting identified as corresponding to the locationdata, competitors of the organization based at least in part on thelocation data.

The foregoing is a non-limiting summary of the invention, which isdefined by the attached claims.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 illustrates one exemplary environment in which embodiments mayoperate;

FIG. 2 is a block diagram of one exemplary system that may analyzelocation data as part of a consumer analytics platform;

FIG. 3 is a block diagram of a second exemplary system that may analyzelocation data as part of a consumer analytics platform;

FIG. 4 illustrates a sample tag cloud of characteristics that can bedetermined, in some embodiments, for a consumer or group of consumers;

FIG. 5 illustrates exemplary analytics available for production by aconsumer analytics platform operating in connection with techniquesdescribed herein;

FIG. 6 is a flowchart of one exemplary process for analyzing consumerbehavior based on location data;

FIG. 7 is a flowchart of one exemplary process for obtaining locationdata for a consumer;

FIG. 8 is a flowchart of one exemplary process for identifying a triptaken by a consumer based on location data;

FIG. 9 is a flowchart of one exemplary process for identifyingcharacteristics of a consumer based on path information;

FIG. 10 is a flowchart of one exemplary process for interpretingconsumer profile data to yield inferences and predictions in connectionwith a study requested by a market researcher; and

FIG. 11 is a block diagram of one exemplary computing device with whichembodiments may operate.

DETAILED DESCRIPTION

Applicants have recognized and appreciated that there are variousdisadvantages associated with conventional techniques for determiningconsumer characteristics, including consumer behavior. Asking a customerto answer a series of written or oral questions could provide inaccurateor incomplete information. Inferences from this data likewise may beinaccurate or incomplete. For example, a customer may accidentallyunderestimate the number of times the customer visits a business or anamount of time spent at each visit to the business. Or, when asked abouta marketing campaign, the customer may misremember about having seen abillboard or other advertisement. Moreover, there may be a high cost orundesirable delay associated with designing and conducting a survey togenerate appropriate data.

Applicants have further recognized and appreciated thatautomatically-collected consumer location information can lead to moreaccurate or more complete consumer analytics. Such automated collectioncould be performed with the permission of individual consumers, butwithout requiring any actions be taken by the individual consumers. Insome embodiments, information about consumers may help businesses makecommercial decisions.

Though, location data collected and analysis performed on that data maybe useful in other environments. Techniques as described herein couldalso provide information for non-commercial organizations about peoplewith which the organizations interact. For example, analysis of locationinformation could provide information to non-profit organizations aboutdonors, to politicians about voters, to governments about citizens, orany other suitable type of organization and a consumer related to thatorganization. It should be appreciated that, as used herein, the term“consumer” is a generic term for a person who interacts with anorganization or who may interact with an organization, and does notimply, by itself, a commercial relationship between the consumer and theorganization.

Regardless of the purpose for which data is being analyzed, consumerswho have opted to participate in a system that gathers data fordetermining consumer characteristics may carry with them portableelectronic devices that have location-determining capabilities. Thedetermined consumer location, from time-to-time, may be communicated toa consumer analytics platform for analysis. Data about a location ofeach consumer can be occasionally collected for each consumer as theconsumers move while going to work, doing errands, going to socialactivities, etc. In some embodiments, a consumer analytics platform mayobtain location data for a consumer using the devices at time intervalsdetermined on a per-consumer basis. The platform may dynamically adjustthe time intervals based on various factors, including a consumer'scurrent location, a current time, and a history of locations visited bya consumer. The intervals between acquiring location information for anyconsumer may be selected to provide relevant information withoutrequiring excessive power usage by the portable electronic device, whichcan quickly drain a battery of the device and may deter consumers fromagreeing to participate in the system.

The location data that is obtained may be obtained from any suitablesource and in any suitable form. As an example, the data may specifygeographic coordinates for a consumer's location and a time at whichthat location data was obtained. In some embodiments, the portableelectronic device may be a cellular telephone or may include cellulartelephone capabilities, and the data may be acquired through the cellphone network. Such data may be acquired using known interfaces to thecellular telephone system, which may generate data based in whole or inpart on cell tower locations relative to the portable electronic device.Such a determination may employ triangulation techniques and may usetechnology sometimes called assisted GPS. Using the cellular telephonenetwork may reduce the power drain on the portable electronic device,because such techniques as assisted GPS use less power than, forexample, GPS. In addition, using a cellular device, or other device thatserves a purpose other than data collection, as the source of locationdata may increase the reliability of consumer data by increasing thelikelihood that a consumer will carry the portable electronic device.

Regardless of the specific source or format of the location data, thelocation data received from multiple consumers may be received andstored for later analysis. When analyzed, this location data couldreveal characteristics of consumers. These characteristics may includebehaviors, such as the stores at which the consumers shop, how long theyspend at each store, and which stores they visit in one overall shoppingtrip. In addition to revealing commercial behaviors, such an analysismay reveal recreational behaviors. Additionally or alternatively, ananalysis of this location information could reveal characteristics suchas consumer preferences. Additionally or alternatively, an analysis ofthis location information could reveal identity characteristics, such astheir home and work locations and roads on which they frequently travel.This information, based on collected factual information and analysis,could be more reliable or more readily obtained than information derivedfrom consumer's answers to questions.

As an example of behavior characteristics that may be derived,information about locations visited and trips taken by consumers may bederived. This information may include determining that a consumervisited a point of interest for a particular study, such as a storeowned by a sponsor of the study or a competitor of that sponsor.Alternatively or additionally, the analysis may reveal a set of allpoints of interest visited by the consumer, and patterns in visits topoints of interest by the consumer. Paths that are sets of points ofinterest visited together by a consumer, such as part of a single trip,and the route between the points of interest can also be determined fromthe location data, as can patterns in paths. For example, the platformmay identify sets of two or more points of interest that the consumeroften visits together in one path.

As an example of preference characteristics that may be derived,location data, defining geographic locations, may be combined with placeinformation, indicating activities that occur at specific geographiclocations at times when the consumer is present at the location to yieldinformation about characteristics of a consumer.

The identity characteristic information may include information abouttypes of organizations the consumer visits, which may reveal interestsof the consumer. As a specific example, if a consumer is detected, basedon the location data, to often visit professional sports venues andsports-themed bars, the consumer analytics platform may identify theconsumer as a sports fan. As another example, if a consumer is detectedto often visit gyms, public sports fields, professional sports venues,and sports-themed bars, the platform may identify the consumer as aperson with an “active” lifestyle. Though, preference characteristicsmay be derived in a more fine-grained way. By correlating location data,including times, with specific events at specific locations at timeswhen a consumer is present, a more accurate determination of apreference may be made. For example, by detecting that a consumer is ata sports venue when a hockey game is on-going, the consumer may beclassified as a hockey fan.

Such information collected for multiple consumers may be used as thebasis for inferences and predictions about groups of consumers, whichmay be provided to an organization who sponsored a study performed withthe consumer analytics platform. In some cases, when characteristics aregenerated through analysis of location data for consumers, thecharacteristics may be stored in profiles for each consumer.Characteristics for each consumer that are stored in the profiles may bereviewed to yield inferences and predictions about consumers withrespect to the organization sponsoring the study. In some cases, theinferences and predictions with respect to the organization may includeinferred or predicted characteristics of the consumers, such asbehaviors of groups of consumers with respect to the organization orrelated organizations. In other cases, the inferences and predictionscould be information about potential outcomes of business decisions,such as outcomes related to each of various proposed scenarios. Forexample, information could be provided, based on the inferences orpredictions, that indicates whether and how consumers may react topotential business decisions or what consumers may do given particularconditions. Any suitable information may be generated as an inference orprediction, based on profile data for multiple consumers.

Those inferences or predictions could aid the organization makedecisions such as which products to sell, marketing campaigns toundertake, locations of new store sites, or other commercial decisions.For example, the consumer analysis system may format the inferences andpredictions to reveal to a business who its competitors are. Competitorsmay be revealed, for example, by showing which businesses are visited byconsumers with characteristics comparable to those of consumers whovisit stores run by the business. Conversely, the consumer analyticsplatform may format the inferences and predictions to reveal to abusiness what businesses are complementary to its business, by showingwhich businesses consumers with comparable characteristics visit inconjunction with the business.

Some inferences and predictions generated by the consumer analyticsplatform may reveal that consumers that have an existing relationshipwith an organization often have existing relationships with otherorganizations, that the consumers live or travel within a certain area,or that some portion of the consumers have a certain preference. Theorganization may also learn that consumers that do not have an existingrelationship with the organization have certain characteristics, such asliving in a certain area or having certain interests. This informationcould then be used by organizations in any suitable manner. For example,an organization could make strategic decisions based on the information.Store siting and marketing campaigns can be influenced by consumercharacteristic information, as stores may be located near consumers'homes or travel routes and marketing campaigns may be directed at knowninterests of consumers.

As another example, inferences generated by the consumer analysis systemmay reveal advertising effectiveness. By recognizing that a consumer hasbeen exposed to an advertisement based on location, the system may thenanalyze captured location data to determine whether a consumer haschanged behavior after having been exposed to the advertisement. Such achange may be the basis of an inference that the advertisement waseffective.

As yet a further example, inferences and predictions may reveal thecontext in which consumers do or do not visit a retail location. Forexample, by identifying from location data a consumer's home and office,a business can determine which types of consumers typically stop topurchase a particular type of product when leaving home, when leavingthe office or in some other context. Such information, for example, mayinform a business of promotions or advertisements that may enticeconsumers, in a context in which they are likely to purchase aparticular product to visit a store operated by that business.

The embodiments described above are merely illustrative of the variousways in which embodiments may operate. Further examples of ways in whicha consumer analytics platform can be implemented in accordance withprinciples described herein are provided below. For ease of description,in the exemplary embodiments below, each consumer is a customer orpotential customer and each organization is a business. As discussedabove, though, embodiments are not so limited. Rather, embodiments mayidentify characteristic information for any suitable group of peoplehaving or potentially having any type of commercial or non-commercialaffiliation with any suitable organization. For ease of explanation,however, any such group of people will be referred to herein as a groupof “consumers.”

In some embodiments described below, electronically-derived consumerlocation data is analyzed to determine information relating tocharacteristics of a consumer, which may include information aboutconsumer behavior. Consumer behaviors include behaviors engaged in byconsumers. Such consumer behaviors may include (1) retail-relevantactivities and (2) lifestyle-relevant activities. Retail-relevantactivities may include behaviors relating to commercial activitiesengaged in by a consumer. Commercial activities include activities inwhich a monetary transaction takes place or could take place, includingvisits to any location where a consumer could purchase products orservices. Lifestyle-relevant activities may include behaviors related toa consumer's daily life. Lifestyle behavior includes information about aconsumer's work life and home life and regular routine, including theirrecreational behaviors. Lifestyle activities include, but are notlimited to, visits to and time spent at a consumer's residence and placeof employment; travel patterns and habits, including commuting patternsand air travel; and visits to outdoor recreation destinations, nightlifelocations, sports and entertainment venues, museums, amusement parks,and tourist destinations.

More particularly, using systems and techniques operating in accordancewith principles described herein, characteristics of consumers may bedetermined through analysis. Characteristics of a consumer may relate toany suitable attributes, such as an identity of a consumer, behavior ofa consumer, and preferences of a consumer. Identity characteristics mayinclude demographic and socioeconomic attributes of a consumer,including where the consumer lives and works. Behavior characteristicsinclude any suitable information on behaviors of the consumer, which mayinclude both retail-relevant behaviors and lifestyle-relevant behaviors.As discussed above, retail-relevant behaviors include behaviors relatingto commercial activities engaged in by a consumer and lifestyle-relevantbehavior includes information about a consumer's work life and home lifeand regular routine, including their recreational behaviors.Characteristics of behaviors may include information about activities inwhich a consumer does or does not participate or a manner in which theconsumer participates in an activity. Information on a manner in whichthe consumer participates in an activity includes information on afrequency or periodicity of the consumer's participation in theactivity. Additionally, guesses as to whether a consumer is likely toparticipate in an activity may be inferred or predicted as part ofbehavior characteristics. Preference characteristics may includeinformation on preferences of the consumer for particular types ofproducts/services or particular products/services, including brandloyalties of a consumer. For each of these characteristics, a strengthof the characteristic and/or a likelihood that the characteristic hasbeen correctly determined may be identified.

Illustrative Context

FIG. 1 illustrates one exemplary environment in which embodiments mayoperate to detect location data for consumers and, by analyzing thatlocation data, determine characteristics of those consumers. The exampleof FIG. 1 is described in connection with one consumer, but embodimentsmay operate with any number of consumers.

In the environment 100 of FIG. 1, a consumer 102, who has decided toparticipate in an analysis program carried out by a consumer analyticsplatform 108, changes location while going to work, going home, going toschool, running errands, or moving from any other place to place. In thespecific example of FIG. 1, the consumer 102 visits a coffee shop 122,gas station 124, workplace 128, restaurant 130, and grocery store 132during a day. The consumer analytics platform 108 monitors movements ofthe consumer 102 and, by analyzing locations the consumer 102 visits,determines characteristics of the consumer 102 and produces inferencesand predictions based on the characteristics.

The consumer 102 is associated with a device 104 that can be used toobtain location information for the consumer 102 as the consumer 102moves. The consumer 102 may move with the device 104, as the consumer102 may carry the device 104 or the device 104 may be embedded in a car,piece of clothing, or baggage carried by the consumer 102. In somecases, the device 104 may be useful only in determining a location ofthe consumer 102, while in other cases the device 104 may have otherfunctionality. For example, the device 104 may be a mobile telephonewith location-identifying capabilities, such as a cellular telephonewith a built-in Global Positioning System (GPS) or Assisted GPS (AGPS)receiver that the cellular telephone can use to determine its currentlocation. The device 104 may be able to communicate with a network 106,which may be any suitable communication network, including a wirelesswide-area network (WWAN). In cases where the device 104 is a cellulartelephone, the network 106 may be a cellular network.

The environment 100 may also include a consumer analytics platform 108that is able to obtain location information for the consumer 102,analyze the location information to determine characteristics of theconsumer 102, and produce inferences and predictions based on thedetermined characteristics. The consumer analytics platform 108 mayobtain location information for a consumer 102 from the device 104. Insome cases, the consumer analytics platform 108 may request the locationinformation from the network 106 and, in turn, the network 106 mayobtain location data from the device 104. In some embodiments, theconsumer analytics platform 108 may request the location data atintervals that the location analysis tool 108 can adjust based onvarious factors, including a current location of the consumer 102.

The consumer 102 may move from place to place during activities engagedin by the consumer. As the consumer 102 moves, the device 104 associatedwith the consumer 102 may determine a location for the consumer 102continuously or occasionally. This location information may then betransmitted to the consumer analytics platform 108 to be analyzed.

In some embodiments, the consumer analytics platform 108 may analyzeinformation about a consumer 102 in the context of paths taken by theconsumer. A path is a movement of a consumer from one endpoint toanother endpoint and possibly through intermediary points, where eachendpoint and intermediary point is a setting. A setting is a geographiclocation visited by a consumer 102 that has some attached significance,such as a point of interest or a personally-relevant location for aconsumer. Points of interest may be, for example, stores at which aconsumer 102 stopped or a location on a road at which a billboard can beviewed, or other places with significance, and personally-relevantlocations may be, for example, a home or workplace for a consumer orother location at which a consumer spends a lot of time. Points ofinterest could also be locations within shopping malls, such as storeswithin a shopping mall, or areas within a store, such as a particulardepartment in a store.

Endpoints of paths are settings where consumers spend a lot of timeand/or are often considered destinations for consumers, such aspersonally-relevant locations for consumers, and therefore provide startand finish points for paths. Using home and place of employment asexamples of endpoints, paths can be taken by consumers from home to oneor more other settings then back to home, from home to work and viceversa, from home to one or more other settings and then to work and viceversa, and from work to one or more other settings then back to work.Other endpoints are possible, and paths can be defined in the context ofany endpoints.

FIG. 1 shows a few examples of paths that may be taken by a consumer 102and that the consumer analytics platform 108 may monitor and analyze.The consumer analytics platform 108 may have information aboutpersonally-relevant locations for the consumer 102, such as a locationof the home and the place of employment for the consumer 102. Thisinformation may have been provided by the consumer 102 or may have beenidentified by the consumer analytics platform 108 by observing that theconsumer 102 spends a lot of time at night in one location, which ismost likely the home of the consumer 102, and spends a lot of timeduring weekdays in another location, which is most likely the place ofemployment for the consumer 102.

FIG. 1 shows that the consumer 102 visited multiple locations in aseries of movements one day and the consumer analytics platform 108determined that these locations are associated with the illustratedsettings 120-132. The consumer 102 started at home 120, visited a coffeeshop 122, visited a gas station 124, drove on a highway 126, spent timeat work 128, visited a restaurant 130, went back to work 128, spent timeon the highway 126, went to a grocery store 132, and returned to home120. As the consumer 102 visits each of these settings, the consumeranalytics platform 108 obtains location data that identifies that theconsumer 102 is at a geographic location associated with the setting.The consumer analytics platform 108 may then match the obtainedgeographic locations to known locations for settings to determine thesetting corresponding to each geographic location. The consumeranalytics platform 108 may then examine these settings and determinefrom them paths taken by the consumer 102, which may include firstidentifying settings that are endpoints. In the example of FIG. 1, thereare two endpoints: home 120 and place of employment 128. From theseendpoints, the consumer analytics platform 108 may determine that theconsumer 102 went on three different paths: Path A home to work; Path Bwork to work; and Path C work to home. During Path A the consumer 102visited the coffee shop 122, the gas station 124, and the highway 126.During Path B the consumer 102 visited the restaurant 130. During Path Cthe consumer 102 visited the highway 126 and the grocery store 132.

The consumer analytics platform 108 may analyze settings visited by theconsumer 102 and the paths taken by the consumer 102, as well as otherinformation about the settings visited by the consumer 102, to determinecharacteristic information for the consumer 102. For example, byexamining the settings, the consumer analytics platform 108 maydetermine from the visits to the coffee shop 122 and the restaurant 130that the consumer 102 regularly purchases meals and does not regularlymake meals. Further, by analyzing path information for the consumer 102,the consumer analytics platform 108 may identify behaviors of theconsumer 102, like that the consumer 102 is a commuter and that theconsumer 102 makes multiple stops during a normal day. The consumeranalytics platform 108 may also determine that the consumer 102 commutesby car, rather than by public transportation. By comparing these pathsto information previously gathered about a consumer 102, moreinformation about the consumer 102 may be determined. For example, ifthe consumer 102 does not regularly visit a grocery store 132 on the wayto home 120 from work 128, and if an advertisement for the grocery store132 or for a food product was located on the highway 126, the consumer102 may be determined to be swayed or swayable by the advertisement orsimilar advertisements.

Through analyzing multiple paths and anchors over time, the consumeranalytics platform 108 may be able to confirm, refine, or correct thesedetermined characteristics of the consumer 102. When other location datais obtained, such as location data collected during weekend activitiesof the consumer 102 or travel activities, that location data can be usedto determine other characteristics of the consumer 102. Informationabout the consumer 102 can be stored in a profile for the consumer 102and can be combined with information about other consumers to determineinformation about the consumers.

The consumer analytics platform 108 may use the characteristicinformation for each consumer in any suitable manner or present thecharacteristic information to any suitable party. In some cases,businesses 110 will request that the consumer analytics platform 108perform a study and provide the business 110 with information aboutconsumers 102, such as information about consumers 102 that patronizethe businesses 110. The consumer analytics platform 108 may then reviewthe characteristics for multiple consumers determined through theanalysis and produce inferences and predictions regarding the consumers.These inferences and predictions may be made based on thecharacteristics determined from the analysis. For example, theinferences and predictions may include additional characteristics thatwere inferred or predicted for a group of multiple consumers. As anotherexample, the inferences and predictions may include information on howconsumers may be expected to react to potential business decisions,including information on potential outcomes of one or more proposedscenarios.

Information yielded by the inferences and predictions that are returnedto the business 110 as results of the study may be used by thebusinesses 110 in any suitable manner. For example, if the coffee shop122 were to discover based on information provided by the consumeranalytics platform 108 that the majority of its customers are carcommuters rather than people who work locally, the coffee shop 122 maydecide to offer more products packaged to be taken in a car. As anotherexample, the coffee shop 122 may identify interests or preferences ofconsumers 102 that live near the coffee shop 122 and go to a competitorcoffee shop, such that the coffee shop 122 could determine how toencourage those consumers 102 to visit the coffee shop 122. Or, if thegrocery store 132 determines that many of its customers live far awayfrom its store, the grocery store 132 may decide to build a new storecloser to those customers and may select a location of the new storebased on routes traveled by consumers, locations of other stores thatthe consumers are detected to visit, or information on potentialoutcomes for each of multiple proposed locations (e.g., numbers ofconsumers that will shop at each proposed store). As another example, ifthe grocery store 132 was running advertisements on the highway 126 thatappeared to convince people who were not planning to visit the store todo so, the grocery store 132 may infer that the advertisements areeffective and continue using those advertisements. Information aboutcharacteristics of consumers 102 can be used in any suitable manner by abusiness 110.

Illustrative Systems

Described below are examples of various systems and techniques that maybe implemented in some embodiments for operating a consumer analyticsplatform to obtain location data for consumers and analyzing thatlocation data to determine characteristics of consumers. Embodiments arenot limited to implementing these exemplary systems and techniques, asothers are possible.

FIG. 2 illustrates one exemplary consumer analytics platform 200 forobtaining and analyzing location data for consumers. In embodiments, theplatform 200 may include one or more consumers such as a consumer 202, aconsumer location data facility 204, and a consumer analytics engine208. As the consumer 202 passes through multiple locations, data abouteach location visited by the consumer 202 may be obtained by theconsumer location data facility 204 and stored. For example, theconsumer 202 may visit a shopping mall, a retail store, workplace,residential place, an entertainment center, and the like. The consumerlocation data facility 204 may obtain location data for each of thelocations through which the consumer 202 passed. The location dataobtained by the facility 204 may be passed to the consumer analyticsengine 208 for analysis. After analysis is carried out, inferences andpredictions based on information about consumers 202 may be provided tomarket researchers 230, such as in response to studies requested by theresearchers 230.

The consumer location data facility 204 may obtain location data from aconsumer 202 in any suitable manner. In embodiments, the consumer 202may have access to an electronic device, such as a location-capableelectronic device, that can be used by the consumer location datafacility 204 to obtain location data for the consumer 202. For example,the electronic device may determine a location of the consumer 202 andtransmit the location to the consumer location data facility 204. Theelectronic device may transmit the location data in response to arequest for location data (e.g., from the consumer location datafacility 204) or of its own initiative. The location of the consumer 202may be obtained using any electronic device. In some cases, theelectronic system may be co-located with the consumer 202. Examples ofelectronic devices include, but are not be limited to, location-awaremobile telephones, GPS-enabled tracking devices, personal navigationdevices, in-car navigation devices, and the like.

Location data may be obtained for each consumer 202 and stored by theconsumer location data facility 204. The location data that is obtainedfor each consumer 202 may include any suitable location information thatcan be received from electronic devices of the consumers 202 ordetermined through analysis. In embodiments, location data for theconsumer 202 may include geographic information for a location, an errormargin for the geographic information, and a time that location wasvisited by a consumer 202. The geographic information may include anysuitable global geographic information, such as latitude and longitude,and/or local geographic information such as street addresses orlocations within buildings. The error margin may identify a range ofother locations near the geographic location that may be the actuallocation of the consumer 202 and allows systems receiving the locationdata to account for imprecision in the identified location.

Some consumers 202 may volunteer to provide their location data, whileothers may be enticed to do so. For example, the consumer 202 may beinterested in providing information to businesses in which the consumer202 is interested (e.g., businesses at which the consumer 202 shops)because the consumer 202 is interested in helping those businesses byproviding them with information. Or, in other cases, the consumer 202may provide location data in exchange for discounts at these businessesor some incentive from an operator of the consumer analytics platform200. In many embodiments, consumer privacy may be important and locationdata is only obtained for consumers when the consumers agree to providethe location data. Though, in some cases privacy may not be a concernand location data for consumers can be retrieved without permission ofconsumers.

The consumer location data facility 204 may receive and store thelocation data of multiple consumers 202 in any suitable manner, asembodiments are not limited in this respect. Location data that isstored by the consumer location data facility 204 may be processed bycomponents of the consumer analytics engine 208, including the anchorand path classification facility 210, to determine further informationabout consumers 202. The location data may be passed at any suitabletime and in response to any suitable conditions.

Consumer location data facility 204 may also obtain location data at anysuitable time. In some embodiments, the consumer location data facility204 may be operated by a same entity that operates the consumeranalytics engine 208 and the facility 204 may actively obtain and storelocation data for the consumer 202, and may pass the location data tothe engine 208 upon obtaining the information. In other embodiments, thefacility 204 may be operated by a different entity and may obtainlocation data only in response to a request from the consumer analyticsengine 208. In some embodiments where the facility 204 is operated by adifferent entity, the facility 204 may be a cellular communicationnetwork with an interface that allows for requesting and receivinglocation data for a particular device attached to the cellularcommunication network. In these embodiments, the interface may be thesame or a similar interface to an interface used for Enhanced 911 (E911)systems to obtain location data from a mobile phone that has made anemergency call. In some other embodiments in which the facility 204includes a cellular communication network, however, the consumeranalytics engine 208 may be able to communicate freely and directly to adevice attached to the cellular network or receive information from adevice attached to the cellular network, or in any other way, ratherthan only communicating via a designated interface.

However the consumer location data facility 204 obtains location data,the consumer analytics engine 208 may obtain and analyze the locationdata. The consumer analytics engine 208 may include various componentsto perform an analysis of location data received from the consumerlocation data facility 204. As shown in FIG. 2, in some embodiments theengine 208 may include an anchor and path classification facility 210,an anchor analysis facility 212, a path analysis facility 214, a pointof interest facility 218, an inference engine facility 220, a tribalclustering facility 222, a prediction facility 224, and a real-timedetection facility 228. The consumer analytics engine 208 analyzeslocation data and is able to identify characteristics for consumersbased on analysis of the location data and is able to produce inferencesand predictions based on the characteristics resulting from theanalysis, while protecting consumer privacy.

In addition to location data, in some embodiments other data may also beprovided to a consumer analytics engine 208. For example, purchase dataand/or demographic data may be made available to the consumer analyticsengine 208.

Consumer purchase data may be provided by the consumer purchase datafacility 206 with or without request by the consumer analytics engine208. Consumer purchase data may include any suitable information aboutconsumer purchases that may be provided by businesses at which consumershop or financial companies with which customers have relationships.Purchase data may also be provided by consumers themselves, such as inresponses to surveys. Businesses may obtain data about consumerpurchases when the consumers provide to businesses personal informationto associate the consumers with purchases. This may be the case when theconsumers participate in programs (e.g., rewards or loyalty programs)with the businesses, such that the consumers identify themselves at thetime they purchase goods or services. Similarly, financial companies mayobtain information about consumer purchases when the consumers usecredit cards, debit cards, checks, layaway programs, or other financialproducts to purchase goods or services.

Demographic data may also be provided by a demographics data facility207 in some cases. Demographics data may be used to identify demographicinformation associated with particular areas. For example, from censusdata and other sources, incomes, education levels, and household sizescan be stored for particular areas like ZIP code areas. This informationcan then be provided to the consumer analytics engine 208 in response toa request from the consumer analytics engine 208 or without a requestfrom the engine 208.

Consumer purchase information may be aggregated for each consumer andprovided to the consumer analytics engine 208 to be analyzed alongsidelocation data for consumers. The consumer analytics engine 208 may jointhe purchase data with the location data in any suitable manner todetermine a correspondence between location data and purchase data forindividual consumers. This join may be carried out in any suitablemanner. For example, if a consumer provides a phone number to businessesor financial companies, that phone number may be provided alongside thepurchase data and may be used to identify location data for the consumerin embodiments where location data is retrieved with the assistance of acellular telephone.

Demographic information may be associated with consumers usingtechniques described below. Briefly, when a place of residence isdetermined for a consumer, demographic information associated with thatcommunity may be retrieved and used to determine characteristics of theconsumer.

The consumer analytics engine 208 may include any suitable componentsfor performing any suitable analysis of location data relating toconsumers 202 to determine characteristics of the consumers 202. Inembodiments, the anchor and path classification facility 210 may receivelocation data for the consumer 202. The anchor and path classificationfacility 210 may receive input in the form of a set of data pointsrepresenting geographic locations visited by a consumer and maydetermine settings visited by a consumer and a path taken by theconsumer to visit the settings.

In some cases, the anchor and path classification facility 210 mayfilter received location data to remove excess or redundant pieces oflocation data. This filtering may include attempting to identify piecesof location data that relate to a same or similar location. Through thisprocess, a number of “anchors” can be determined that are geographiclocations at which a consumer stopped. Each anchor may be related to oneor more pieces of location data, depending on a frequency with whichlocation data was obtained for the consumer and how long the consumerspent at the anchor. Analyzing anchors rather than analyzing all of thelocation data for a consumer may be useful, as identifying places atwhich a consumer stopped or spent a great deal of time may provide moreinformation about characteristics of a consumer than locations throughwhich a consumer passed without stopping.

To identify anchors, the anchor and path classification facility 210 maycluster sequential location points for a consumer 202 to identifylocation points that are related in time or distance. For example, sucha clustering of the sequential location points may be carried out usingEuclidian distance clustering. In one example of a Euclidean distanceclustering, locations within 400 feet of one another may be identifiedas being related to a same potential anchor. Additionally, by comparingtime differences between location points related to the same potentialanchor, a duration of time spent by consumer 202 at the potential anchorcan be determined. Each cluster of locations associated with a durationabove a threshold, such as duration of greater than ten minutes, can beidentified as an anchor. An anchor, in embodiments, may then be definedfor the consumer 202, based on the location data, that represents asimilar location and a corresponding time interval. The anchor and pathclassification facility 210 may store as a location of the anchor acalculated location for the anchor, which may be an output of amathematical operation involving individual location data points for theanchor. In some embodiments, the calculated location for an anchor maybe a geometric mean of the individual location data points associatedwith the anchor. The anchor and path classification facility 210 mayalso store the individual location data points associated with ananchor.

Once anchors are identified, the anchor and path classification facility210 may define a set of anchors as a path. A path is a set of anchors,with a route between them, that a consumer 202 visited in series. A pathincludes two anchors that are endpoints and may or may not includeanchors that are intermediary points, depending on what the consumer wasdoing and where the consumer stopped. As discussed above, the endpointsmay be settings known to be associated with the consumer 202 and thatmay be considered ultimate destinations when a consumer 202 istraveling. Endpoints include personally-relevant locations forconsumers, including places of residence and employment for the consumer202, but may be anywhere that marks the ultimate destination or end ofan outing. Intermediary anchors may be settings that the consumer 202visited during a path. For example, during a shopping trip on theweekend, the two endpoints for the trip may be the home of the consumer202 and intermediary points may be stores and restaurants that theconsumer 202 visited after leaving home and before returning home.

Identification and analysis of anchors and paths by the anchor and pathanalysis facility 210 may be aided by an anchor analysis facility 212and a path analysis facility 214. Information about locations, clusters,anchors, and paths may be provided to one or both of the anchor analysisfacility 212 and the path analysis facility 214.

The anchor analysis facility 212 may generate from location dataregarding locations visited by a consumer 202 a list of unique physicallocations visited by each consumer 202 that can be used by the anchorand path facility 210 to identify anchors. This unique list may also beanalyzed to determine patterns in places visited by the consumer 202.

In some embodiments, the anchor analysis facility 212 may maintain ordetermine some information for each location in the set of uniquelocations. For example, a number of times that a consumer 202 visits thelocation may be identified and times of day the consumer 202 has visitedor typically visits the location can be identified. A frequency of visitor time interval between visits may also be identified for the locationand the consumer 202. If multiple pieces of similar location data areused to identify a location as an anchor in one path, information abouttimes at which the location was visited may be used to determine alength of a visit to an anchor during a path. When location data iscollected for multiple paths, average lengths of visits or patterns inlengths of visits may be identified.

Anchor analysis facility 212 may also analyze anchors to identify thosecorresponding to settings that are personally-relevant locations for aconsumer 202, including identifying locations corresponding to places ofresidence and employment of the consumer 202. To do so, anchorscorresponding to locations that a consumer 202 often visits and wherethe consumer 202 spends many hours can be identified. Next, time-of-dayand day-of-week criteria may be applied to those anchors. Thetime-of-day and day-of-week criteria may be used to make infer whetherthose anchors correspond to personally-relevant locations. For example,based on these criteria, an anchor at which the consumer 202 spendseight hours during the day on weekdays may be the place of employmentfor the consumer 202 and an anchor at which the consumer 202 spendseight hours during the night on weekdays may be the place of residencefor the consumer 202. Other criteria may be used to similarly identifyother personally-relevant locations. These personally-relevant locationsfor a consumer may be identified as potential endpoints and may be usedby the path analysis facility 214 to identify paths.

The path analysis facility 214 may analyze information regarding pathsidentified by the anchor and path analysis facility 210, as well as aidthe facility 210 in identifying paths. As discussed above, a path can beidentified as a set of anchors and a route between anchors that is boundby a beginning endpoint and an ending endpoint. When a path isidentified, the path analysis facility may analyze the path to determineinformation about the path. For example, the facility 214 may perform aquantitative analysis on a path to identify quantitative attributes ofthe path. Quantitative attributes include, but are not limited to, atotal distance traveled, an average speed of travel, and a pathduration. The path analysis facility 212 may also identify qualitativeattributes of a path, including whether the path was one-way orround-trip by determining whether the endpoints are the same anchor, ordetermining a type of transportation used during the path by analyzingthe route taken and the speed of travel. Other qualitative attributesinclude a purpose of the path, which may be inferred through analyzingthe attributes of anchors visited during the path. Settingscorresponding to anchors visited during a path may be identified usingthe point of interest facility 218, which is discussed below. If onlyone anchor was visited and the anchor corresponds to a store, then thepath may be related to shopping for a particular item or type of item.If the anchors of a path are each related to stores of a same type, thenthe path may be related to shopping for a particular product or type ofproduct. If the anchors of a path are related to multiple stores of adifferent type, then the path may be related to a general shopping tripfor many different types of items. Paths may be related to otheractivities, not just shopping. If a path includes a visit to a publicpark or public playing field, the path may be related to exercising. Ifthe path includes a lengthy visit to an anchor very far away from thehome of the consumer 202, then the path may be related to a vacation orbusiness trip taken by the consumer 202. Any suitable attributes ofanchors may be used to identify a purpose of a path.

Patterns in paths may also be identified by the path analysis facility214. For example, when purposes of paths are identified, particulartypes of paths may be analyzed. For example, a quantitative analysis canbe carried out on a type of path to determine an average length of thattype of path in distance and/or in time, or an average length of timebetween paths of that type. Patterns in paths can also be identifiedbased on settings corresponding to anchors in paths, such as how often aconsumer 202 visits two particular settings together in a path and howoften the consumer 202 visits two particular settings in differentpaths. Similarly, patterns can be detected in how often anchors ofparticular types are visited together in the same paths or in differentpaths. Patterns in attributes of paths can also be compared to settings.For example, patterns in length of paths that include a particularsetting or type of setting can be determined, and patterns in purpose ofpaths that included a visit to a particular setting or type of settingcan be identified. Any suitable patterns can be identified to yield anysuitable information about paths of a consumer 202.

The anchor and path classification facility 210, the anchor analysisfacility 212, and the path analysis facility 214 were all discussedabove in the context of determining information about anchors and pathsvisited by a single consumer 202. In some embodiments, these facilitiesmay also determine information about multiple consumers 202. Patterns inanchors and paths for multiple consumers 202 could be identified. Any ofthe exemplary types of patterns described above could also be determinedacross multiple consumers 202.

The anchors discussed above that were determined based on the locationdata are locations identified by groups of location data obtained for aconsumer 202. Additional information about a location may be determinedby identifying a setting corresponding to a location. A setting may be aplace associated with a location, such as a business or office that isassociated with a geographic location of the business/office, that isassociated with some meaning, such as being associated with somebehavior or type of behavior. Information about settings may be usefulin analyzing anchors and paths, as the settings can provide informationabout activities in which a consumer may have engaged at that geographiclocation, which could provide more information on characteristics of theconsumer.

The point of interest facility 218 of the consumer analytics engine 208may provide additional information that may be useful in analyzinganchors and paths. For an anchor, the calculated location (e.g.,geometric mean location) of the anchor may be cross-referenced to a dataset of settings maintained by the point of interest facility 218. Thedata set of the point of interest facility 218 may include informationon geographic locations and activities associated withpersonally-relevant locations for individual consumers and with pointsof interest (POIs) that include places that consumers may visit. EachPOI may be a place that a consumer 202 could visit, such as an office,shop, concert venue, restaurant, or other places.

A setting in the POI data set may be defined in part by a geographiclocation for the POI. The geographic location for the POI may be definedand stored in any suitable way, including as a point or a polygon. Wherethe location is defined by a point, the point may be associated with alatitude/longitude corresponding to the point and a radius around thepoint. Where the location is defined by a polygon, edges and vertices ofthe polygon may be each defined by a latitude/longitude. When acalculated location for an anchor and/or other locations within theerror margin for the calculated location of the anchor fall within theradius of a point or within the edges of the polygon, the anchor may bedetermined to correspond to that POI and, accordingly, the consumer 202may be determined to have visited that POI.

In some cases, determining to which setting a geographic locationvisited by a consumer or an anchor relates may include choosing betweenmultiple settings. This may be the case where the error margin indicatedby the location data overlaps with the locations (e.g., the polygon orthe point and radius) for multiple different settings. In such a case, aparticular setting to which the location data corresponds may beselected in any suitable manner. For example, a probability may becalculated for each potential setting that each potential setting is thesetting visited by the consumer. Such a probability may be calculatedbased on information about the location and/or about the consumer. Wheninformation about the location is used, then a setting closest to thegeographic location of the consumer may be selected or a setting with alocation area (e.g., the polygon or the point and radius) having thegreatest overlap with the area of the error margin for the consumer maybe selected. When information about the consumer is used, theninformation about settings previously visited by the consumer, which maybe derived from information like purchase data provided by consumerpurchase data facility 206, may be used to select a most likely settingvisited by the consumer. For example, if two potential locations are afast food establishment and a sporting goods store, and the consumer hasnever visited a fast food establishment but often visits sporting goodsstores, then the more likely setting may be determined to be thesporting goods store. When information about the location and/or theconsumer is used, probabilistic inference techniques may be used to makethe determination of the probabilities associated with each setting. Forexample, the problem may be modeled using a Bayesian Network such as aHidden Markov Model. When a Hidden Markov Model is used, the hiddenstate may be the visited setting and location data for the consumer maybe input as observations. The Hidden Markov Model may then be evaluatedusing techniques like the Viterbi algorithm to determine the most likelysetting visited by the consumer.

As discussed above, a type of setting or an activity engaged in by aconsumer 202 may be used to make determinations about a consumer 202 orabout paths taken by the consumer 202. Accordingly, the setting data setmay include information about each setting.

In some cases, information about behaviors may not be known forpersonally-relevant locations that are identified by the anchor analysisfacility 212 and may not appear in the data set, while in other casesthe behaviors may be identified based on assumptions about a type of thepersonally-relevant location (e.g., home or work location).

Each POI, however, may be associated in the data set with at least onedescription of the POI and at least one categorization of the POI. Insome cases, a type of POI or a type of activity engaged in at the POImay be the same at all times. In this case, information about the POIcan be retrieved and analyzed once the geographic locations aredetermined to match. In other cases, however, a type of POI or theactivities for a geographic location may vary based on time. Forexample, a POI that is a restaurant at mid-day may become a nightclub atnight. As another example, an arena may host basketball games, hockeygames, and concerts at different times. For these POIs, a time that theconsumer 202 visited the POI may be used to determine the type of POI oractivities in which the consumer 202 engaged at the POI.

As a result, in some embodiments, POIs may be categorized in the dataset of the POI facility 218 based on location and time. The locationcategorization may include a categorization of the types of activitiesin which a consumer 202 could engage at the POI at any time. Forexample, a location categorization may indicate that a POI is a sportsvenue, quick-service restaurant, low-cost retailer, or other type oforganization. A time-based categorization may indicate, of thelocation-based categories, a type of activity in which a consumer 202could engage at a particular time. The time-based categorization of thedata set of the facility 218 may be populated by externally-availableinformation about the POI. For example, event schedules, transitschedules, air travel schedules, and the like and may be retrieved for aPOI and stored and used to determine activities in which a consumer 202could engage at a time and, from that, a time-based categorization ofthe POI.

Using the location and time-based categorization, each POI may beassigned to one or more defined category of activities related to POIs.In an exemplary scenario, POIs may be categorized as relating torestaurants, lodging, parks and recreation, sports and fitness,nightlife, sites of outdoor or indoor advertisements (e.g., billboards),school/university, pharmacies, supermarkets, and work places, amongothers. When a consumer 202 is determined to have visited a POI, acategory of POI may be selected based on factors like time, andinformation about the POI may be provided for analysis. For example, theinformation about the POI may be used by the facilities 210, 212, and214 as discussed above.

The consumer analytics engine 208 can also analyze the informationcollected from location data and data sets, and from consumer purchasedata facility 206 and the demographic data facility 207, to determinecharacteristics of the consumers. The characteristics of a consumer 202that may be determined through this analysis include characteristics ofan identity of the consumer 202, behaviors of the consumer 202, andpreferences of the consumer 202. Further, behaviors of the consumer 202may be used to determine categories of behavior in which the consumer202 engages and behavior groups to which the consumer 202 thereforebelongs.

To perform this analysis, the consumer analytics engine 208 may analyzeinformation received from the consumer location data facility 204 anddetermined by the anchor analysis facility 212, path analysis facility214, and point of interest facility 218. The consumer analytics engine208 may use any suitable computer learning technique to identifyrelationships between locations, consumers, anchors, and paths, andpatterns in those relationships. For example, based on information aboutone consumer a relationship may be established between two anchors thatidentifies that a consumer that visits one anchor is somewhat likely tovisit the other anchor. Similarly, relationships may be identifiedbetween paths or attributes of anchors and/or paths. These relationshipsmay be adjusted as information about other consumers is reviewed. Forexample, if another consumer is detected to visit the same two settings,then a relationship between the settings may be strengthened. On theother hand, if another consumer is detected to visit one setting and notthe other, a relationship between the settings may be weakened.Relationships can be both positive and negative, such that arelationship could indicate either that two settings are very likely tobe visited together or are very unlikely to be visited together.

Data regarding the strength/weakness of these relationships may bestored in any suitable manner, including using confidence values. As theconsumer analytics engine 208 examines the data for consumers andestablishes and adjusts relationships, the consumer analytics engine 208may assign confidence values to the established relationships indicatinghow likely or true the engine 208 believes the relationship to be. Theseconfidence values may be adjusted over time, as the consumer analyticsengine 208 learns more and becomes more or less confident in particularrelationships.

The relationships learned by the consumer analytics engine 208 can beused to analyze the location data, anchors, paths, and patterns forconsumers to determine characteristics of consumers.

Based at least in part on these relationships, the consumer analyticsengine 208 can generate guesses regarding characteristics of a consumer202. These relationships can be used to determine, when a consumer 202matches one side of a relationship, how likely the consumer 202 is tomatch the other side of the relationship when there is no data availableto indicate directly whether the consumer 202 matches the other side ofthe relationship. As a specific example, if the consumer 202 is detectedto visit a first POI but not a second POI, and the engine 208 hasdetected a relationship between the first and second POIs, the engine208 may determine how likely the consumer is to visit a second POI. Inthese cases, the strength of the relationship as determined by thelearning algorithm can determine the likelihood of the consumer 202matching the second part of the relationship.

The consumer analytics engine 208 may determine characteristics in anysuitable manner. In some embodiments, the engine 208 may examinepatterns in paths and/or anchors, and/or patterns in purchase data, toinfer characteristics of a consumer 202. For example, the engine 208 mayexamine patterns in the settings and the types of settings visited bymultiple consumers 202 and the times of those visits. Patterns insettings may be defined by patterns in repeat visits to a particular POIor by repeat visits to a category of POI. Patterns in times may bedefined by patterns in, for example, the time of the day when POIs werevisited, day of the week for the visited POIs, seasonality and durationof each visit, the speed of travel between locations, etc. Such patternsmay be identified based on location data and/or purchase data forconsumers.

By examining these patterns, various conclusions could be drawn. Forexample, the engine 208 may determine whether a consumer that hasvisited certain POIs or takes certain paths is likely to visit aparticular POI. As another example, the engine 208 may determineinformation about the regularity of the daily routine of a consumer 202and then make inferences regarding whether the consumer 202 is likely tomaintain an unvarying schedule and whether the consumer 202 is likely tovisit different POIs or different types of POIs. This may be useful indetermining how likely a consumer 202 is to be swayed to visit a POIthat the consumer 202 has not previously visited, including POIs thatthe consumer 202 regularly passes but does not visit. Similarly,frequency of visits to a particular POI and POIs that are frequentlypassed but not visited may be used by the engine 208 to infer strengthof brand preferences and loyalties of a consumer 202. For example, if aconsumer 202 visits two stores of the same type, but visits one morefrequently than the other, the engine 208 may infer that the consumerprefers the more-visited store to the other.

Patterns in paths, such as frequency or timing with which paths of acertain type are made by a consumer 202, may yield inferences aboutbehaviors of the consumer 202 or preferences the consumer 202 has forpaths with certain purposes. For example, if a consumer 202 isdetermined to be visiting many car dealerships in multiple paths, theconsumer 202 may be inferred to be shopping for a car. Similarconclusions can be made about shopping for homes by analyzing patternsin visits to real estate brokers, banks, and/or open houses,particularly if those visits depart from previous behaviors of aconsumer 202. Similarly, when a consumer 202 often visits sports venuesand sports bars, the consumer analytics engine 208 may infer that theconsumer 202 is a fan of sports, while if the consumer 202 often visitsgyms and public playing fields in addition to sports venues and sportsbars, the consumer 202 may be inferred to be an “active” person.Deviations from patterns may also be notable, such as when a consumer202 visits a setting they have not previously visited or at a time thatthe consumer 202 does not typically visit that setting. If anadvertising campaign is underway for the setting, the consumer analyticsengine 208 could infer from the deviation in the consumer's behaviorpatterns that the consumer 202 was swayed by the advertising campaign.The engine 208 may also make this conclusion if the analysis shows theconsumer 202 passed by a setting associated with a billboard used by theadvertising campaign, and thus likely viewed the billboard, prior todeviating from the behavior pattern.

Characteristics may also be determined by the consumer analytics engine208 by comparing location data and data about settings visited byconsumers to demographic data from the demographic data facility 207.For example, when a consumer's place of residence is identified using,for example, techniques described above, demographics associated with aconsumer's community may be used to identify characteristics of theconsumer, such as income, education, and family size characteristics,among others.

Characteristics of a consumer determined by the consumer analyticsengine 208 may also be entered into the tribal clustering facility 222.The tribal clustering facility 222 clusters consumers' patterns andbehaviors into tribes, which are behavior groups associated with one ormore consumer characteristics. A tribe may be established around anysuitable characteristic(s), including lifestyle-relevant behaviors,retail-relevant behaviors, places visited, schedules, preferences, andother characteristics. Some tribes may be related to particular marketsegments, such as demographic segments or consumption habit segments,and other tribes may be related to lifestyle habits like recreationalinterests and regularity of schedules.

Exemplary tribes that may be monitored and maintained in someembodiments include a home-oriented tribe for people who are often athome; a work-oriented tribe for people who are often at work; a commutertribe for consumers who travel long distances between home and work;“early riser” and “late-riser” tribes dependent on when a consumerleaves their home for the day; a nightlife tribe for consumers who areoften out late at night; an “active lifestyle” tribe for consumers whoare detected to be partake in athletic activities (e.g., visit gyms andpublic playing fields); sports fans and sub-tribes for fans ofparticular teams and/or sports for consumers who are detected to go tosporting venues and sports bars; store-based tribes for consumersdetected to often shop at particular stores; shopping tribes forconsumers who have particular shopping habits, like single-storeshopping trips, multi-store shopping trips, following a strict shoppingroutine, and shopping for a particular item (e.g., car, home, etc.);frequent flier tribes; frequent overnight traveler tribes; and tribesrelating to whether a consumer has been or potentially has been exposedto an advertisement of a marketing campaign (e.g., a billboard). Aconsumer could be identified as belonging to one or more of these tribesand/or other tribes based on obtained location data and informationderived from analysis by facilities 208, 210, 212, and 214.

The tribal clustering facility 222 may maintain information aboutmultiple different tribes and may determine, based on informationdetermined by the consumer analytics engine 208, whether a particularconsumer 202 belongs to a tribe. This may be done by comparingrequirements or conditions for each tribe to information known about aparticular consumer 202. If the information known about the consumer 202from the analysis of the engine 208 matches the conditions/requirementsof a tribe, then the consumer may be determined to be in the tribe. As aspecific example, the “sports fans” and “active lifestyles” tribes mayhave the requirements discussed above—visits gyms and public playingfields for “active lifestyles” and goes to sporting venues and sportsbars for “sports fan”—and a consumer 202 may be associated with thesetribes when the consumer analytics engine 208 determines that theconsumer 202 has characteristics meeting those requirements. As anotherexample, the engine 208 may determine through its analysis that aconsumer 202 is a frequent flier when paths of the consumer 202 ofteninclude two anchors separated by a large difference in time and distancewith no location points in between. This difference could indicate thatthe consumer 202 traveled on a plane between the two anchors. When theseanchors are noticed multiple times by the engine 208, the engine 208 maymark the consumer 202 as a flyer. When the tribal clustering facility222 observes the mark relating to the consumer 202, the facility 222 mayidentify that the consumer 202 is in the frequent flier tribe.

Of course, as discussed above, the consumer analytics engine 208 mayadjust relationships and conclusions over time, as the engine 208 learnsmore about a relationship. Further, habits of a consumer 202 may changeover time. As such, a consumer 202 that is placed into a tribe may beremoved from a tribe if information about the consumer 202 changes forany reason.

Location data, purchase data, and/or information determined about aconsumer 202 may be stored by the consumer analytics engine 208 in aprofile for the consumer 202. Profiles may be similarly maintained foreach consumer 202 for which the consumer analytics engine 208 obtainslocation data and perform analysis. The profiles for each consumer 202can include any of the characteristics determined by the consumeranalytics engine 208 or facilities included by the consumer analyticsengine 208, including identity, behavior, and preferencecharacteristics. A profile may be stored and formatted in any suitablemanner, as embodiments are not limited in this respect. In someembodiments, a single contiguous data structure may store thecharacteristic information for a profile for one consumer, while inother embodiments characteristic information may be stored for oneconsumer in multiple different data units.

By storing characteristics in profiles for each consumer, thecharacteristics can be later reviewed and used in consumer studies toidentify further consumer analytics. In some embodiments, the consumeranalytics engine 208 may receive requests for studies to be performed tofurther identify the characteristics of consumers, such as from marketresearchers 230. Results of a study can be based at least in part onreview of characteristic information included in profiles for consumers,which may yield information related to a topic of the study. Forexample, review of the characteristics may yield information related toa business (or other organization) sponsoring the study, or otherbusinesses (or other organizations) related to the business sponsoringthe study.

Computations based on these characteristics may yield inferences andpredictions for the consumers 202 based on the characteristics. Asillustrated in FIG. 2, an inference facility 220 and prediction facility224 are included in the consumer analytics engine 208 and may be used toproduce inferences and predictions from the profile data for consumers202.

The inferences and predictions made by facilities 220, 224 may beperformed in the context of a study requested by a market researcher andsurrounding a particular topic. Accordingly, the inferences andpredictions may be related to the topic of the study. For example, whena study is requested on behalf of a particular business, inferences andpredictions may be made regarding consumer characteristics that arerelated to that business. As another example, inferences and predictionsmay be made about what consumers may do given one or more conditions orhow consumers may react in a proposed scenario or in each of multipleproposed scenarios. Consumer characteristics related to a business mayinclude characteristics of consumers' interactions with the businessand/or interactions with related businesses including competitors andbusinesses of the same or similar type. Consumer characteristics mayinclude identity, behavior, and preference characteristics for consumersthat are related to the business, including what types of consumersinteract with the businesses, how or when the consumers like to interactwith the business, or how likely particular types of consumers are tointeract with the businesses in the future.

The inferences and predictions of facilities 220, 224 may be based on alearning algorithm that identifies relationships, similar torelationships described above. The learning algorithm may identifypatterns in characteristics of consumers from the profile data and usethose relationships to identify characteristics related to the topic ofthe study. Inference facility 220 may use the relationships to determinecurrent characteristics of consumers related to the business, includingcurrent identities, behaviors, and preferences of consumers with respectto the business. Prediction facility 224 may use the relationships todetermine future characteristics of consumers related to the business,including future identities, behaviors, and preferences related to thebusiness.

A specific example of a study is one commissioned by a business that isa restaurant, to determine characteristics of its customers. A currentcharacteristic that can be inferred by the inference facility 220 isthat consumers are more likely to visit the restaurant for lunch whengoing on long-duration, general shopping trips and than when on a shortshopping trip for a particular item. This may be based on an inferenceregarding detected behaviors of consumers, that the restaurant was mostoften visited by consumers during paths that were identified to be“general shopping” trips and that were long. A future characteristicthat can be predicted by the prediction facility 224 is that manyconsumers will visit the restaurant on a particular holiday weekend.This may be based on a detection that consumers most often engage ingeneral shopping on holiday weekends, as well as that consumers mostoften visit the restaurant during “general shopping” trips, such thatthe prediction facility 224 may predict that many consumers will be ongeneral shopping trips on the particular holiday weekend.

In this way, the consumer analytics engine 208 may obtain location dataand/or purchase data, perform analysis on the location data and/orpurchase data to identify characteristics of consumers, and then reviewthe characteristics and compute inferences and predictions based on theconsumer characteristics and behaviors.

The inference facility 220 and the prediction facility 224, whengenerating inferences and predictions, may generate confidence valuesthat indicate how confident the facilities are in theinference/prediction, which can indicate how likely theinference/prediction is to be true. These confidence values may berelated to the strength of the relationships, determined by the learningalgorithm, on which the inferences/predictions are based. In someembodiments, these confidence values can be output as part of theinference/prediction, such that someone reviewing theinference/prediction may be aware of the strength of theinference/prediction.

As mentioned above, a study to be performed using the consumer analyticsengine 208 may be requested by a market researcher 230. Marketresearchers 230 (including both professional market researchers andlaymen performing market research) may wish to determine moreinformation about consumers 202 that are customers of or potentialcustomers of a business or a type of business, or may wish to know moreabout consumers 202 with respect to any other topic. For example, themarket researchers 230 may wish to know about the identities ofconsumers 202, such as demographic characteristics for consumers 202that regularly visit the business, that have visited the business, orthat regularly pass by the business but have not visited. The marketresearchers 230 may also wish to know about inferred preferences ofconsumers 202 that have not visited a business but have visited thebusiness' competitors. Similarly, the market researchers 230 may wish toknow how many consumers 202 passed by an advertisement and subsequentlyvisited a business associated with an advertisement, including those whovisited the business for the first time. Such inferences and predictionsmay be yielded from the analysis of the profile data, including thelocation data, purchase data, and/or information yielded from analysisof the location and/or purchase data.

The interface by which the consumers 230 may query the data set mayallow for any suitable queries to be made, including any suitable filterterms or conditions. For instance, a market researcher 230 may assembleone or more set of queries that attempt to collect data regarding aspecific consumer sample population. Additionally or alternatively, thequeries may be created with conditions that attempt to focus results toas to answer specific questions posed by or to the market researcher orto try to solicit information with various levels of detail. Answers tothe queries may be provided by engine 208 based on inferences andpredictions drawn from the obtained location data and the results of theanalysis performed on the location data, which are stored in theprofiles for each consumer.

Once market researchers 230 have the information from the consumeranalysis engine 208, the researchers 230 and/or the businesses withwhich the researchers 230 may be affiliated may make decisions using theinformation. For example, store siting decisions could be made with thisinformation. Once a set of characteristics associated with consumersthat visit a store or a type of store are determined, queries can bemade for places of residence or employment for consumers that matchthose characteristics. Additionally, places of residence or employmentfor consumers that already visit the store can be determined. Distancesthat consumers travel or will travel to the type of store can bequeried, as well. Once these places and distances are determined, thebusiness can determine where to place a store that will have a goodlikelihood of being visited by existing or potential new consumers.Advertising effectiveness can also be determined based on results ofqueries to the engine 208 regarding consumers that potentially viewed anadvertisement and subsequently visited or purchased goods or services ata business associated with the advertisement (e.g., an advertisedbusiness or a business selling an advertised product). As anotherspecific example, a competitor analysis can be carried out to determine,based on characteristics of the consumers, which businesses consumersview as alternatives and characteristics of consumers that visit orpurchase goods or services at each business.

In embodiments, any suitable queries may be submitted by marketresearchers 230 to yield inferences and predictions from the consumeranalytics engine 208. In some embodiments, access to information storedby the platform 200 may be limited to only queries for inferences andpredictions, rather than to data collected about individual consumers,due to privacy concerns. Raw information about each consumer 202 (e.g.,raw location data not yet analyzed) or information that could identifyindividual consumers 202 rather than classes of consumers 202 may beconfidential and may be appropriately secured for privacy. Inembodiments, consumer privacy can be protected by limiting access for aresearcher 230 of the platform 200 to querying and receiving theinference/prediction output of the consumer analytics engine 208, ratherthan examining data about individual consumers 202. Further, someembodiments may provide information about groups of consumers 202,rather than information about individual consumers 202, or may providecharacteristics information in a way that cannot be linked to anindividual. Such a system enables maintaining confidentiality of theidentity of consumers 202 and the raw data stored in the consumerlocation data facility 204.

Information about consumers 202 may be used by the platform 200 not onlyin response to queries by market researchers. Additionally oralternatively, in some embodiments a real-time detection facility 128may react in real time to inferences/predictions about consumers as theinformation about consumers is obtained or determined through analysis.The real-time detection facility 128 may react to the information byissuing a real-time response to any suitable party. The party mayinclude a consumer 202, an organization, and adjustable advertisement,among others. The real-time response may be, for example, a delivery ofan advertisement or message to the consumer 202 regarding a topic inwhich the consumer 202 may be interested, based on inferences orpredictions regarding the consumer 202 at that time. For example, if theconsumer 202 is detected to be visiting particular types of stores andthe consumer analytics engine 208 may predict that the consumer 202 willsoon try to find a restaurant, the real-time detection facility 128could present information to the consumer 202 to encourage the consumerto visit a particular restaurant. In another example of a real-timeresponse, information about consumers 202 may be presented to anadjustable advertisement such that the advertisement can be adjusted tosuit the consumer 202 as the consumer 202 passes by the advertisement.Information about a consumer 202 can also be provided to a business inresponse to a consumer 202 visiting the business or interacting with thebusiness. For example, discount coupons for the consumer 202 orinformation about the consumer 202 that could be used in negotiationwith the consumer 202 over a sale may be presented to the organization.These discount coupons or information about the consumer 202 may bepresented at any suitable time, including when the consumer 202 firstvisits an organization or when the consumer 202 begins a purchase at apoint of sale.

The consumer analytics platform 200 may be used to obtain location dataregarding a consumer and, from the location data, determinecharacteristics of consumers. These characteristics can be used toproduce inferences and predictions regarding the consumers, such as inresponse to consumer analytics studies requested by market researcherson behalf of businesses. In this way, in some embodiments consumeranalytics can be determined based on location data obtained forconsumers as the consumers move and engage in activities at variouslocations.

Various techniques that may be carried out by the components of aconsumer analytics platform like the one described above are describedin greater detail below in connection with FIGS. 5-9. However, it shouldbe appreciated that embodiments are not limited to operating with theplatform 200 of FIG. 2 or with any particular type of consumer analyticsplatform. Other platforms are possible. FIG. 3 illustrates anotherexemplary platform 300 with which some embodiments may operate.

FIG. 3 illustrates a second consumer analytics platform 300 and showsentities that may interact in the consumer analytics platform 300. Theplatform 300 is similar in some ways to the platform 200 illustrated inFIG. 2 and discussed above. Accordingly, operations of the components ofthe platform 300 may be described in the context of correspondingcomponents in the platform 200 of FIG. 2.

In the platform 300, an entity for obtaining location data for consumersis separate and distinct from an entity for analyzing location data todetermine characteristics and producing inferences and predictions basedon the characteristics. More particularly, the consumer location datafacility 304 may be provided with location data regarding consumers 302by a network facility 316 that is operated by one entity. The consumerlocation data facility 304 may then provide location data to anotherentity that operates the consumer analytics engine 306. As discussedbelow, the entity operating the network facility 316 and the entityoperating the consumer analytics engine 306 may cooperate to obtainlocation data for consumers and analyze that location data.

The platform 300 includes one or more consumers 302, a consumer locationdata facility 304, a network facility 316, and a consumer analyticsengine 306. As the consumer 302 moves about and visits a number ofsettings at different geographic locations, data relating to eachgeographic location visited by the consumer 302 may be gathered usingnetwork facility 316 and the consumer location data facility 304.

Location data may be stored in the consumer location data facility 304in any suitable manner, as embodiments are not limited in this respect.In some cases, one or more location data points for a consumer 302 maybe stored in the facility 304 and may be associated with an identifierfor the consumer 302. The identifier that is used may be any suitableidentifier, including ones that anonymize or attempt to anonymize thelocation data by making the location data difficult to match to anindividual. For example, a mapping table may be maintained in theconsumer location data facility 304 that provides one-to-one associationbetween a unique identifier of a consumer 302 with the most recentlocation data for the consumer 302. The unique ID that is used in themapping table may be an International Mobile Equipment Identity (IMEI)of an electronic system accessed by the consumer, a unique PersonalIdentification Number (PIN) assigned by the network operator of theconsumer, or some other type of identifier.

The consumer location data facility 304 implemented in the platform 300in any suitable manner that allows for location data to be communicatedto other entities. In some embodiments, the consumer location datafacility 304 may be located in a server that may also include theconsumer analytics engine 306, such that the consumer analytics enginecan obtain location data locally by querying a data store on the samemachine. In other embodiments, the consumer data location facility 304may be installed in electronic devices associated with the consumers302. The consumers 302 may each be associated with electronic devicesthat include location-identifying capabilities. The electronic devicesmay be, for example, location-aware mobile telephones, GPS-enabledtracking devices, personal navigation devices, in-car navigationdevices, and the like. In still other embodiments, the consumer datalocation facility 304 may be installed in equipment of a networkfacility 316 operated by a network operator. The network operator mayprovide network services to the consumer 302. The network facility 514may be a network setup such as a Public Land Mobile Network (PLMN) orother wireless wide area network (WWAN) deployed by a mobile networkoperator.

Regardless of where the consumer location data facility 304 is locatedor which entity manages the facility 304, location data for a consumer302 may be provided to the consumer location data facility 304 and thelocation data may be provided from the consumer location data facility304 to a consumer analytics engine 306. In some embodiments, locationdata for consumers 302 may be obtained in real time, meaning that as aconsumer 302 moves the location of the consumer 302 is continuouslyupdated in the consumer location data facility 304. In otherembodiments, the location data may be stored in the consumer locationdata facility 304 at discrete times and made available for later use. Inembodiments that obtain location data for a consumer 302 discretely(rather than continuously), the location data may be obtained at anysuitable interval or in response to any suitable condition. In someembodiments, the location data may be obtained in response to receipt ofa location data request from the consumer analytics engine 306 maytransmit a location data request to the consumer's electronic system.The location data request may include a request for current location ofthe consumer 302 that identifies the consumer 302 according to theidentifier used by the mapping table of the consumer location datafacility 304 (e.g., the IMEI).

The location data request may be received by the network facility 316 orconveyed to the network facility 316 by the consumer location datafacility 304. In embodiments where the network facility 316 is managedby a mobile network operator and is associated with a mobile network,the location data request may be received by the network facility 316via an interface designated for requesting and transmitting locationdata. In some cases, the interface may be an interface associated withan Enhanced 911 (E911) system. The E911 system allows for retrieval oflocation data for mobile phones during emergency situations, but networkoperators are able to make this interface available for other situationsand can do so in these embodiments.

The location data request, when received by the network facility 316,may be forwarded to a receiving facility 318 residing in the networkfacility 316. The reception of the location data request may trigger atransmitting facility 320 in the network facility 316 to initiate atransmission to location-determination hardware, such as GlobalPositioning System (GPS) hardware, in the electronic device of aconsumer 302 for whom the location data was requested.

The electronic device may identify location using any suitabletechnique, including various techniques known in the art. Using sometechniques, the electronic device may determine the location alone andtransmit the determined location data to the network facility 316. Usingother techniques, the network facility 316 may cooperate with theelectronic device to determine the location data. Techniques that may beused include cell identification, enhanced cell identification,Uplink-Time difference of arrival, Time of arrival, Angle of arrival,enhanced observed time difference (E-OTD), GPS, Assisted-GPS, hybridpositioning systems, Global Navigation Satellite System (GLONASS), theGalileo navigation system, location-determination services using accesspoints for wireless local area networks (WLANs), and the like. Inembodiments, the location data may additionally or alternatively beobtained using paging, triangulation, and the like.

Using these or other techniques, the electronic device and the networkfacility 316 may acquire geographic information identifying a currentlocation of the consumer 302, such as latitude and longitude of thecurrent location of the consumer 302. The geographic information, alongwith a corresponding time frame and an error margin for the geographicinformation (collectively referred to as “location data”), may be storedin the consumer location data facility 304 along with an identifier forthe consumer 302. The location data may then be made available to theconsumer analytics engine 306 by the consumer location data facility304, including being transmitted to the consumer analytics engine 306.

In embodiments, the consumer analytics engine 306 may receive multiplepieces of location data for the consumer 302 over time, which will be inthe form of a set of data points each identifying a location throughwhich the consumer 302 passed. As discussed above in connection withconsumer analytics engine 208 of FIG. 2, the consumer analytics engine306 may generate a unique list of physical locations visited by eachconsumer 302 by identifying anchors from locations that are similar intime and space and by identifying settings corresponding to theseanchors. By analyzing this unique list, patterns can be identified inthe settings that can be used to determine some characteristics of aconsumer 302. For example, an identity, behaviors, and preferences ofthe consumer 302 can be identified through analysis. Additionally,personally-relevant locations for the consumer 302, such as the place ofresidence and place of employment of the consumer 302, can be determinedthrough analysis.

Analysis of location data can be performed by any suitable components ofthe consumer analytics engine 306. As illustrated in FIG. 3, theconsumer analytics engine 306 may include a behavior analysis facility308, an inference engine facility 310, a profile creation facility 312,and a mapping facility 314. Through these and/or other components, theconsumer analytics engine 306 may perform analysis of location dataregarding the locations visited by the consumer 302. The consumeranalytics engine 306 may also review the characteristics of the consumerand compute inferences and predictions of characteristics of theconsumer 302 in response to requests to perform a study received from amarket researcher 330 or other entity.

In some embodiments, the consumer analytics engine 306 may operatesimilarly to the consumer analytics engine 208 of the platform 200 ofFIG. 2, but embodiments are not limited to generating characteristics inany suitable manner. Examples of characteristics that may be generatedthrough this analysis include consumer lifestyle-relevant behaviorinferences and retail-relevant behavior inferences based upon theoutputs of the consumer behavior facility 308. In some embodiments,these behavior inferences may detect patterns in one or more manners,for example, the types of places of interest (POIs) visited by eachconsumer, the time of the day when the POI was visited, day of the weekfor the visit to the POI, seasonality and duration of each visit to aPOI, the speed of travel between POIs, the regularity of each consumer'sdaily routine and travel, commute patterns, the frequency of visit to aparticular location, an inferred nature of the trip, brand preferences,what locations are passed but not visited, and the like.

In some embodiments, characteristics determined by the behavior analysisfacility 308 from the inference engine facility 310 may be provided tothe profile creation facility 312. The profile creation facility 312 maybe adapted to create profiles for consumers 302 based on informationabout the consumers 302 obtained via the location data or determined bythe behavior analysis facility 308 and the inference engine facility310. In some embodiments, the profile creation facility 312 may store ina profile only information determined by the consumer behavior facility308 and may not perform any analysis or determination itself. In otherembodiments, however, the profile creation facility 316 may detectpatterns in the profile information received from other sources and maystore in a profile additional information about a consumer. Profilescreated for each consumer by the profile creation facility 312 may bestored in a profile data set accessible by the consumer analytics engine306.

Profile information, once stored in a profile data set by the profilecreation facility, may also be analyzed by the mapping facility 314. Themapping facility 314 may maintain information correlating profileinformation for consumers 302 with other information including otherprofile information and information relevant to organizations to whichthe consumers 302 could be related. The mapping facility 314 may, forexample, maintain mappings between some characteristics of a consumer302 and other information or characteristics, such that when a profileof a consumer 302 is detected to include one piece of information, adecision may be made about the consumer 302. In some embodiments,additional information may be stored in a profile for the consumer 302upon detecting a match. For example, a further characteristic of theconsumer 302 may be determined based upon a detected match in a mapping.In some embodiments, one or more actions can be taken upon determiningthat a consumer 302 matches a mapping.

Once characteristics for consumers 302 are determined by the consumeranalytics engine 302 and stored in profiles by the profile creationfacility 312, the characteristics associated with each profile may bereviewed to yield inferences and predictions. The inferences andpredictions may be produced as part of determining results of a studyrequested to be performed by a market researcher 330. The requestedstudy may be directed to a particular business or other topic and theinferences and predictions may generate information related to theparticular business or other topic. For example, consumercharacteristics related to a business may be inferred or predicted,which may include characteristics of consumers' interactions with thebusiness and/or interactions with related businesses includingcompetitors and businesses of the same or similar type. Consumercharacteristics may include identity, behavior, and preferencecharacteristics for consumers that are related to the business,including what types of consumers interact with the businesses, how orwhen the consumers like to interact with the business, or how likelyparticular types of consumers are to interact with the businesses in thefuture. As another example, information about how consumers may act inthe future, given various conditions, or may react to proposed scenariosmay be inferred or predicted. Examples of inferences and predictions arediscussed above in connection with FIG. 2.

In the platform 300, the inference engine facility 310 may receiveinputs from the consumer behavior facility 308 and may be able to readinformation from profiles generated for consumers by the profilecreation facility 312 and location data obtained from consumer locationdata facility 304. The inference engine facility 310 may generateinferences and predictions for consumers, relating to the business orother topic of the study, based on the information from facility 308 andthe profiles.

Thus, when market researchers 330 enter queries for studies, inferencesand predictions may be generated based on location data and/orcharacteristics of consumers determined from the location data. Whenresults including the predictions and inferences are received inresponse, the results may aid the market researcher 330 in determiningthe identity or characteristics of consumers, such that decisions can bemade by businesses with accurate information about consumers that areexisting or potential customers of the businesses.

Using any of the exemplary systems described above or the exemplarytechniques described below, various characteristics of consumers can bedetermined from location data and stored in profiles for each consumer.These characteristics may include identity, behavior, and preferencecharacteristics, among others. In some embodiments, when characteristicsare determined for a consumer, a word or phrase may be associated withthe consumer, such as in the profile maintained for the consumer. FIG. 4illustrates one exemplary set of characteristics that may be determinedby exemplary embodiments for a consumer and maintained in a profile. Thecharacteristics, which may also be called “tags,” that are associatedwith a consumer include information on an identity of the consumer, likethat the consumer is a resident of Somerville, Mass., USA, and works indowntown Boston. The characteristics also include behaviorcharacteristics, including that the consumer is a “CVS regular” and a“McDonald's Patron,” and that the consumer goes to the movies on Fridaysand the grocery store on Wednesdays. Preference characteristics, likethat the consumer is a Celtics fan, may also be stored.

Information that is stored for each consumer may be queried by marketresearchers or others in any suitable manner. In some embodiments,market researchers and others may be able to navigate a menu systemrelating to characteristics of consumers or services that can be offeredthat use information relating to characteristics of consumers. FIG. 5illustrates one exemplary menu of categories of information that may beprovided by a consumer analytics engine, including links betweencategories that are related. By selecting any of the boxes in the topportion of FIG. 5, information about characteristics of consumers can bedetermined. Services rendered by a consumer analytics engine can betriggered by using any of the boxes along the bottom line of the menu ofFIG. 5. For example, reports can be generated or predictions can beoffered on consumers by selecting appropriate boxes in the menu of FIG.5.

Illustrative Techniques

Described above are various systems and platforms for analyzing locationdata to determine characteristics of a consumer, as well as someexemplary types of characteristics that can be determined. Discussedbelow are exemplary techniques that may be carried out in someembodiments to obtain location data, determine characteristics ofconsumers based on the location data, and infer and predict othercharacteristics in response to a request to perform a study. Embodimentsare not, however, limited to carrying out any of the exemplarytechniques described below, as others are possible.

FIG. 6 illustrates one example of an overall process for determiningcharacteristics of consumers and using those characteristics in makingmarket decisions for businesses.

The process 600 of FIG. 6 begins in block 602, in which location data isobtained for a consumer. Any suitable location data may be obtained forthe consumer, including geographic data identifying a current location,a margin of error that identifies the precision the geographic data, andtime data identifying a time the geographic data was obtained. Thegeographic data may be any type of information identifying a location ofa consumer, including a latitude/longitude, a street address, aplacement in a building, or other location data.

The location data may be obtained in part using an electronic deviceassociated with a consumer, such as a device carried by the consumer orintegrated into an item associated with the consumer (e.g., integratedinto a car, baggage, or clothing). The electronic device may obtainlocation data or be used in obtaining location data, and the locationdata may then be transmitted to a consumer analytics platform at anysuitable time and in any suitable manner. In some embodiments, theelectronic device may continuously or occasionally transmit locationdata for the consumer to a consumer analytics platform, while in otherembodiments the consumer analytics platform may occasionally requestlocation data from the electronic device and the electronic device maytransmit the location data upon receipt of the request.

Once the location data is obtained by the consumer analytics platform,the location data may be processed and analyzed in various ways todetermine characteristics of a consumer. In block 604, the locationsvisited by a consumer may be compared to known geographic locations todetermine settings visited by a consumer. The settings may bepersonally-relevant locations known to be associated with the consumer,such as a place of residence or employment, or known points of interest(POIs) that can be visited by consumers. These settings visited by aconsumer may be identified by first identifying, from the raw locationdata, a group of geographic locations at which a consumer stopped. Thegeographic locations at which a consumer stopped are referred to asanchors herein. When a set of anchors visited by a consumer has beendetermined from the location data, the set may be analyzed to determinea path taken by the consumer. A path is a trip taken by a consumer thatincludes settings, bound by two endpoints and possibly includingintermediary points. The two endpoints of a path are settings at which aconsumer spends a lot of time and that a consumer would consider a finaldestination of a trip, which could be personally-relevant locations forthe consumer, like a place of residence and a place of employment. Onceendpoints have been determined in a set of anchors visited by aconsumer, paths may be identified between the endpoints that, based onthe set of anchors and the actual route taken by the consumer, mayinclude zero, one, or more anchors as intermediary points of the path.From examining the types of settings that correspond to each anchorvisited on a path, the types of settings that correspond to theendpoints for the path, or other properties of the path, a purpose of apath may be determined. The purpose of the path may be the consumer'sreason for traveling to and between the settings corresponding to theanchors. Some or all settings corresponding to the anchors may beassociated with categories or descriptions that identify a consumer'sreason for visiting the setting, which could provide insight into thepurpose for the path. For example, if a consumer visits a number ofclothing stores during a path, the consumer may have been shopping forclothes. If the consumer visits a number of stores of different typesduring the path, the consumer may have been on a generic shopping trip.If the consumer visited a number of public parks, museums, landmarks,etc., then the consumer may be determined to have been recreating.

When anchors, settings, and paths have been determined from the locationdata, the anchors and paths can be analyzed to determine characteristicsof the consumer that visited the settings and traveled the paths. Asdiscussed above, determining characteristics of the consumer includesdetermining attributes of a consumer's identity, behaviors, andpreferences. At least some of these characteristics may be determinedfrom detecting and analyzing patterns in the settings and pathsdetermined in block 604. Accordingly, in block 606, the settings andpaths are analyzed to determine patterns. These patterns may be detectedin any suitable properties of the settings and paths. These patterns mayinclude patterns in particular settings visited, types of settingsvisited, times the settings were visited, frequency of visits tosettings or types of settings, settings or types of settings visitedtogether in paths, lengths of paths, frequency of paths with particularpurposes, and other patterns.

The patterns that are detected in block 606 may be patterns for aparticular consumer or patterns for all consumers, based on analyzingtogether the location data, settings, and paths of the consumers.Patterns in settings and paths between consumers may then be determinedand could be used to better understand individual consumers anddetermine characteristics of individual consumers.

The process 600 continues obtaining location data in block 602 andanalyzing the location data in blocks 604 and 606. Additionally, theresults of the analysis of block 606 can be used in block 608

In block 608, the patterns detected for a particular consumer or for allconsumers are used to determine characteristics of the particularconsumer. The characteristics of the consumer can be determined in anysuitable manner, including by analysis, inference, and prediction. Insome cases, for example, by analyzing the settings, paths, and patterns,some characteristics of the consumer can be identified. For example, bydetermining that a likely place of residence for a consumer is inSomerville, Mass., USA, a consumer analytics platform may determine theidentity attribute “Resident of Somerville, Mass.”As another example, bynoting that the consumer visits many gyms and public parks, the consumeranalytics platform may determine that the consumer is interested inphysical fitness. As another example, by noting that the consumer visitsone chain of grocery stores exclusively, the consumer analytics platformmay determine that the consumer prefers that grocery store over others.

Once characteristics of each of multiple particular consumers aredetermined from the analysis of the settings and paths, thecharacteristics may be used by market researchers to make marketdecisions. In block 610, a request to perform a study of consumercharacteristics for a particular business or other topic is received.The request may be received from a market researcher seeking to knowmore about consumers as they relate to the particular business or othertopic. In response to receiving the request to perform the study,characteristics related to the multiple consumers may be retrieved andanalyzed with respect to the particular business or other topic. Forexample, consumers' past interactions with the particular business, withother businesses of the same type, of other businesses in a samegeographic area as the particular business, may be evaluated. Based onthese past interactions by multiple consumers, inferences andpredictions can be produced. For example, inferences can be drawnregarding current characteristics of groups of consumers with respect tothe particular business, including identities of consumers who do and donot interact with the business, behaviors of consumers in interactingwith the business, and preferences of consumers with respect to thebusiness. Similarly, predictions can be made about futurecharacteristics of groups of consumers with respect to the particularbusiness. As another example, information regarding what consumers maydo given one or more conditions or how consumers may react in each ofone or more proposed scenarios may be generated as a prediction orinference. These inferences and predictions may be generated in anysuitable manner, including using machine learning algorithms asdiscussed above.

Any suitable future or current characteristics of consumers with respectto a business can be produced as an inference or prediction in block610. For example, by determining the places of residence and employmentand travel patterns for consumers that are customers or are potentialcustomers of a business, the business can determine a good place tolocate a store. In some cases, potential locations for businesses can beevaluated to determine potential numbers of consumers that would visiteach potential location, as part of determining which location is best.As another example, by determining characteristics of consumers, thebusiness can determine an advertising campaign to undertake. Similarly,by detecting consumers that passed through a location associated with anadvertisement for a business and then visited the business or visitedthe business in a different way than previously (different frequency ordifferent time interval), an inference regarding the effectiveness ofthe advertising campaign can be made. As another example, by examinerinteractions of consumers with particular sets of businesses of aparticular type, competitors to a particular business can be inferred.Once competitors are identified, decisions can be made regarding how toattract consumers away from competitors.

In block 612, after inferences and predictions are produced regardingcharacteristics of consumers with respect to a business, the inferencesand predictions can be output as results of the study requested in block610. The inferences and predictions can then be used to make marketdecisions for the particular business that was the topic of the study.

After the characteristics to be used in marketing decisions are outputin block 612, the process 600 continues determining new characteristicsin block 608 and using the new characteristics in marketing decisions inblock 612.

FIG. 6 describes generally a process that can be carried out fordetermining and using characteristics of consumers through obtaininglocation data for the consumers. FIGS. 7-9 show specific processes thatcan be implemented in some embodiments for carrying out some of thetasks described generally in connection with FIG. 6.

In some embodiments, location data may be transmitted from an electronicdevice associated with a consumer to a consumer analytics platform aftera time interval, or may be requested by the consumer analytics platformafter a time interval. The time interval can be any suitable intervaluseful for monitoring movements of a consumer. In some cases, theinterval may be fixed, while in other cases, the interval may beadjusted.

FIG. 7 illustrates one example of a process for adjusting a timeinterval by which location data is obtained. These techniques may beused by an electronic device determining when to transmit location dataand/or by a consumer analytics platform determining when to requestlocation data, or may be used by any other entity.

The process 700 of FIG. 7 begins in block 702, in which a time intervalby which to obtain location data is first determined. The time intervaldetermined in block 702 may be a default time interval that is used forconsumers when time intervals are first being determined or may be atime interval related to the consumer in some way. In cases where theconsumer analytics platform does not have information about a consumer,such as where the consumer is first being tracked, a default timeinterval may be used. In cases where information is available about theconsumer, however, a time interval may be determined in block 702 basedat least in part on information about the consumer. For example, if theconsumer is known to move frequently, the time interval may be shorterthan if the consumer did not move frequently. In cases where theconsumer moves frequently at some times and less frequently at othertimes, a length of the time interval may be determined in part by a timethe determination is made. For example, a short time interval can beused when the consumer can be expected to be moving and a long timeinterval can be used when the consumer can be expected not to be moving.

Regardless of the time interval selected in block 702, in block 704 newlocation data is obtained by the consumer analytics platform accordingto the time interval. In some embodiments, the new location data may beobtained when an electronic device associated with a consumer detectsexpiration of the time interval, determines a current location of theconsumer, and transmits location data to the platform. In otherembodiments, the platform may detect expiration of the time interval andrequest location data. In any case, location data is obtained by theconsumer analytics platform according to the interval.

In block 706, the new location data obtained in block 704 is used toadjust the time interval by which location data is obtained. This may bedone so as to produce more information about a location of a consumerwhen more accurate information would be useful and produce lessinformation about a location of a consumer when accurate information isnot as useful. In block 706, this adjustment is made according to thecurrent location and movement of a consumer. The current location may bedetermined based on the new location data received in block 704 and themovement of the consumer may be determined by comparing the new locationdata to previously-received location data. Movement information for theconsumer may include information on a speed and direction of movement ofthe consumer.

To adjust the time interval, a current location and movement of theconsumer may be compared to anchors associated with known settings,including points of interest (POIs), to determine whether the consumeris at or approaching a setting. If the consumer is at a setting, thenthe time interval may be decreased so that more location data isobtained while the consumer is at the setting and an accurate length oftime that the consumer spent at the setting can be determined. If theconsumer is near a setting, then a movement of the consumer may beevaluated to determine whether the consumer is moving toward or awayfrom the setting. If the consumer is moving toward the setting, then thetime interval may be decreased such that whether the consumer visitedthe setting can be accurately determined. On the other hand, if theconsumer is moving away from the setting, then the time interval may belengthened. In some cases, when previous location data for a consumerwas last obtained more than a threshold time ago, it may be difficult todetermine accurately a current movement of the consumer. In such cases,when a consumer is detected to be near a setting, another piece oflocation data may be quickly obtained and used to determine accurately amovement of a consumer.

In block 708, behaviors of a consumer may also be used to adjust a timeinterval. For example, current behaviors of the consumer inferred fromthe consumer's location as well as past behaviors engaged in by theconsumer may be used to adjust the time interval. If a consumer isdetermined from the location data to be at work, and the consumertypically does not leave work during the day, then a time interval maybe left unchanged or increased such that fewer pieces of location dataare collected while the consumer is at work and not moving. Similarly,if the same consumer is detected to be on a highway on the way to work,the time interval may be increased for the same reason, before theconsumer reaches work, based on the knowledge about the consumer'santicipated behavior. On the other hand, if the same consumer isdetected to be at work and the current time is near the end of theconsumer's typical work day, the time interval may be decreased suchthat location data may be captured that accurately portrays themovements of the consumer after work.

After the time interval is adjusted based on the location and movementof the consumer and consumer behaviors, the process 700 returns to block704 and obtains new location data based on the adjusted interval. Theprocess 700 then continues with obtaining location data and adjustingtime intervals.

In some embodiments, rather than only increasing or decreasing a timeinterval in blocks 706 and 708, a time interval may be left unchangedbased on an evaluation of the location, movement, and behavior of theconsumer.

Further, while in some embodiments a time interval may be freelyadjusted and may be decreased to as small an interval as possible, inother embodiments limits may be set on the time interval. In someembodiments, for example, an electronic device used to obtain locationdata for the consumer may be battery powered, such as a battery-poweredmobile phone of the consumer. In these cases, a limit may be imposed ona length of the time interval to prevent location data from beingobtained very frequently using the electronic device, which may run downthe battery on the electronic device. This limit may be a limit on howshort a time interval can be. For example, limits may be used such thatthe time interval must be longer than one minute or longer than fiveminutes, though any suitable limit may be used.

As discussed above, once location data is obtained describing movementsof a consumer, the location data can be analyzed in various ways todetermine characteristics for consumers. One way in which the locationdata can be analyzed is by contextualizing the location data. Thelocation data can be contextualized by identifying settings visited by aconsumer (e.g., points of interest (POIs)) and paths taken by a consumerthat include the settings.

FIG. 8 shows one exemplary process 800 for identifying settings andpaths. Process 800 begins in block 802, when location data is obtainedidentifying locations through which the consumer passed. As discussedabove, the location data may include geographic data, a margin of errorfor geographic data, and time data.

Multiple pieces of location data may be obtained in block 802. Thesepieces of location data may not correspond to different places visitedby a consumer, however. If a consumer spends a long time shopping at astore, for example, multiple pieces of location data may be obtained forthe consumer while the consumer is in the store. Each of those multiplepieces of location data may therefore relate to the same place.

In block 804, location data for places visited by a consumer isclustered such that similar location data is grouped together toidentify anchors. This clustering may include clustering based onsimilarity in space and/or in time, which may be done using thresholdsto identify similarity in space and/or time. For example, two pieces oflocation data that indicate geographic locations within 400 feet of oneanother may be clustered. In some cases, these thresholds may beadjusted based on the error margin of location data points. For example,a threshold distance for clustering may be greater when the error marginof associated location data points is larger. Additionally oralternatively, in some cases, these thresholds may be adjusted based ona location and/or movement of a consumer. For consumers in New YorkCity, for example, a threshold distance for clustering may be smallerthan for consumers in Wyoming. Similarly, if a consumer is moving slowly(e.g., walking) then a threshold in time may be shorter than if aconsumer is moving quickly (e.g., driving on a highway). Once pieces oflocation data are clustered to identify anchors, a calculation may beperformed to identify attributes for an anchor. For example, an averagelocation of the geographic location of the multiple pieces of locationdata can be determined, as can an average time, beginning time, endtime, duration, aggregated error margin, or other location attributes.

In block 806, a comparison is made of clustered locations for anchors toa data set of settings. The data set of settings may include informationabout settings at which a consumer may stop. Settings include knownpoints of interest (POIs) like known stores, restaurants, offices, etc.,as well as personally-relevant locations for a consumer like places ofresidence and employment. Each setting may be associated with a locationand a consumer may be detected to have visited a setting when thelocation for an anchor matches a location for a setting. As discussedabove, a location for a setting may be defined by a point and athreshold radius or as a polygon with marked edges and a consumer may bedetected to have visited the setting when the location data (or alocation within the margin of error indicated by the location data ofthe geographic location indicated by the location data) falls within theradius or the polygon. As discussed above, when a location potentiallymatches multiple settings (e.g., when the location, with the errormargin, matches multiple settings) information about locations and/orconsumers may be used to determine to which setting the locationcorresponds. For example, Bayesian Network techniques like Hidden MarkovModels may be used, as discussed above in connection with FIG. 2.

From the comparison in block 806, a set of settings visited by aconsumer may be identified. The settings may be identified based on asequential order in which the settings were visited from the time dataincluded in the location data obtained for the consumer.

In block 808, from the settings identified in block 806, paths may beidentified. As discussed above, a path includes two endpoints and mayinclude intermediary points. Endpoints of the path are settings that aconsumer would consider a final destination of at trip, includingpersonally-relevant locations. For example, endpoints may be places ofresidence or employment for the consumer. Intermediary points may besettings of the sequence that are visited between endpoints. Byanalyzing the sequence of settings identified in block 808, endpointscan be identified and paths can be identified based on the endpoints.

In some embodiments, determining a path may also include determining apurpose for the path. A purpose for a path may be determined throughanalyzing settings visited on the path, including types of settingsvisited on the path. The types of settings visited by a consumer mayindicate a purpose of the consumer in taking the path, including genericshopping, shopping for a specific item, or recreation.

Once paths are identified, the process 800 ends. The paths and settingsthat are identified can then be analyzed to determine characteristics ofthe consumer, including to identify identity, behavior, and preferenceattributes for the consumer. For example, by analyzing settings visitedby a consumer, brand loyalties or behavior patterns can be determinedfor the consumer.

FIG. 9 shows one example of a process for determining characteristics ofa consumer. Prior to the start of process 900 of FIG. 9, location datahas been obtained for locations visited by a consumer and analyzed todetermine settings and paths visited by the consumer. In the process900, patterns in settings and paths visited by a consumer and by otherconsumers are identified and used to determine characteristics of aconsumer.

The process 900 can be carried out using results of any suitable machinelearning technique. In some cases, a machine learning technique mayreview information about consumers, settings, and paths and identifyrelationships between pieces of information. Some relationships that maybe identified include patterns.

The process 900 begins in block 902, in which settings and paths of aconsumer are analyzed to determine patterns. Patterns that may bedetected for settings are discussed above in connection with the anchoranalysis facility 212 of FIG. 2. Such patterns include patterns insettings visited, in types of settings visited, in times that settingsor types of settings were visited, and lengths of time spent at asetting, among others. Patterns that may be detected for paths arediscussed above in connection with the path analysis facility 214 ofFIG. 2. Such patterns include patterns in lengths of paths, in purposesof paths, in times paths of a particular purpose were taken, in settingsthat are visited together in paths or not visited together in paths, andin lengths of time between paths of a particular purpose, among others.

In block 904, characteristics are determined for a consumer based atleast in part on an analysis of the location data, settings, and paths,as well as on the patterns identified in block 902. Determiningcharacteristics of a consumer may be carried out in any suitable manner.For example, some information that is merely factual or can be distilledfrom the information may be determined through the analysis of block904. These characteristics may include identity characteristics, likethat the consumer often drives on highways or is often near a particulartype of setting (e.g., a particular chain of stores). As anotherexample, behavior information like that the consumer visits a particularcoffee shop nearly every day may be determined from the analysis.Characteristics may also be deducted from the available information.Such deduction may be used to determine characteristics that cannot beidentified with certainty from pure analysis. For example, if theconsumer visits a coffee shop nearly every day, it can be deduced thatthe consumer drinks coffee. However, this cannot be known with certaintybecause the consumer may visit the coffee shop for some reason otherthan to drink coffee. As another example, if the consumer spends manyhours nearly every night in a single location, a system may deduce thatthe consumer lives in that location. In another example, if the consumeroften visits gyms and public parks, that the consumer is an athleticperson or a person with an active lifestyle may be deduced. Similarly,if the person often visits sports venues and sports bars, the consumermay be deduced to be someone interested in sports. When such deductionis used, the deduction may be associated with a likelihood that thededuction is correct. This likelihood may be related to a strength of arelationship identified by a machine learning algorithm used in makingthe deduction. A strength of a characteristic may also be determined,such as by how often the consumer exhibits the characteristic or on whatdata the characteristic is based.

In block 906, the information available about the consumer is analyzedto determine one or more tribes to which the consumer belongs. Asdiscussed above, a tribe is a group of consumers sharing particularcharacteristics. Each tribe may be defined by a set of one or morecharacteristics and when a consumer matches those characteristics, theconsumer may be determined to be a part of the tribe. Examples of tribesare given above in connection with the discussion of the tribalclustering facility 222. Such examples include a home-oriented tribe forpeople who are often at home; a work-oriented tribe for people who areoften at work; a commuter tribe for consumers who travel long distancesbetween home and work; “early riser” and “late-riser” tribes dependenton when a consumer leaves their home for the day; a nightlife tribe forconsumers who are often out late at night; and an “active lifestyle”tribe for consumers who are detected to be partake in athleticactivities (e.g., visit gyms and public playing fields). Other tribesare possible.

In block 908, information about anchors and paths for the consumer,characteristics determined in block 904, and the tribes identified inblock 906 are stored in a profile for the consumer in block 908. Storingthe information in a profile allows for the information to be retrievedlater, such as upon receipt of a request from a market researcher toperform a study on data managed by a consumer analytics platform, asdiscussed above in connection with FIGS. 2, 3, and 6. In some cases,storing the information in block 908 may include editing or removinginformation previously stored in the consumer profile. For example, if afirst characteristic is determined for a consumer at a first time, andat a later time a second, conflicting characteristic is determined forthe consumer at a second time, the first characteristic may be removedor edited. In some other cases, the first and second characteristics maybe merged in some way, or the first characteristic may be refined basedon the second characteristic. In some embodiments, how the first andsecond characteristics are handled may be based on a relative likelihoodthat the characteristics are correct or other strength of thecharacteristics, such that whichever characteristic is stronger is thecharacteristic maintained in the database.

Once the information is stored in block 908, the process 900 ends. Afterthis process, further location data may be obtained and the process 900may be again carried out to refine or correct characteristics determinedin block 900. Additionally, market researchers may query the profiles todetermine answers to questions they have about markets for particularproducts, services, or businesses.

While not illustrated in the example of FIG. 9, as discussed above insome cases characteristics may be determined based on purchase dataand/or demographic data, in addition to location data. Embodiments thatreview purchase data and/or demographic data may do so in any suitablemanner, including as in the examples described above.

FIG. 10 illustrates one exemplary process that may be used forperforming a study on characteristics of consumers related to aparticular topic, such as a particular business. Prior to the process1000 of FIG. 10, location data for multiple consumers may be obtainedand analyzed to determine characteristics for the consumers. Thecharacteristics for the consumers may be stored in profiles for eachconsumer. The profiles may be used in the process 1000, in aggregate, todetermine the characteristics of consumers with respect to theparticular topic.

Process 1000 begins in block 1002, in which a request to perform a studyrelating to a topic is received by a consumer analytics platform. Therequest to perform the study may indicate any suitable constraints ordesired outputs of the study. For example, a particular topic of thestudy and may include desired characteristics for consumers to bedetermined as part of the study. Additionally, in some cases the requestmay include characteristics of consumers to be considered as part of thestudy, such that only certain consumers or types of consumers areincluded in the study. Other inputs, such as inputs related toparticular questions to be considered as part of the study, may beconsidered. For example, if the study is being performed to determineoutcomes for different options for a market decision (e.g., differentlocations for new stores), the different options may be provided asinput to be evaluated by the consumer analytics system.

In block 1004, the profiles for multiple consumers for the system areretrieved, each of which indicates characteristics for the consumers. Insome cases, all of the profiles for consumers maintained by the consumeranalytics platform are retrieved in block 1004, while in other cases,when the request of block 1002 indicates required characteristics forconsumers, the profiles of consumers matching those characteristics areretrieved.

In block 1006, the study is performed by the consumer analytics systemby reviewing the profiles retrieved in block 1004 and performing amachine learning process on the profiles and characteristics of theprofiles. As part of the machine learning algorithm, constraints orinputs provided in block 1002 may be considered and used to guide themachine learning process. As part of the machine learning, relationshipsbetween consumers, settings, paths, or other items may be determined.These relationships may be used to determine an output of the machinelearning algorithm. As part of the output, in some cases an examinationcould be performed on the characteristics of groups of consumersincluded in the algorithms, and this examination may yield inferencesand predictions about the consumers with respect to the topic of thestudy. For example, the inferences may identify current characteristicsof the consumers with respect to the topic and the predictions mayinclude potential future characteristics of the consumers with respectto the topic of the study. As another example, inferences or predictionsabout what consumers may do given one or more conditions or howconsumers may react in one or more proposed scenarios may be generated.This information may be described in terms of objectives for a topic ofthe study, such as sales numbers, numbers of customers, or customerthroughput for a business, or other pieces of information that may berelevant to the topic.

A specific example of a study mentioned above is one commissioned by abusiness that is a restaurant, to determine characteristics of itscustomers. A current characteristic that can be inferred in block 1006is that consumers are more likely to visit the restaurant for lunch whenon long-duration, general shopping trips than when on a short shoppingtrip for a particular item. This may be based on an inference regardingdetected behaviors of consumers, that the restaurant was most oftenvisited by consumers during paths that were identified to be “generalshopping” trips and that were long. A future characteristic that can bepredicted in block 1006 is that many consumers will visit the restauranton a particular holiday weekend. This may be based on a detection thatconsumers most often engage in general shopping on holiday weekends, aswell as that consumers most often visit the restaurant during “generalshopping” trips, such that the consumer analytics platform may predictthat many consumers will be on general shopping trips on the particularholiday weekend.

Once the inferences and predictions are produced in block 1006, in block1008 results of the study can be returned to a requestor of the study tobe used by the requestor in making market decisions. The results thatare returned may include the inferences and predictions produced inblock 1006.

Once the results of the study are returned in block 1008, the process1000 ends.

The process 1000 of FIG. 10 may be used in any of various contexts toaid market researchers in making market decisions. As discussed above,market researchers may perform any suitable query to determine anysuitable information about consumers tracked by a consumer analyticsplatform. Market researchers may also use results of the queries in anysuitable manner. In some cases, store siting may be performed based onresults of studies, including inferences and predictions. Store sitingincludes determining for a business good locations to open new stores.Store siting choices may be made based on home and work locations ofexisting or potential customers, routes travelled by existing orpotential customers, and likelihood that potential customers will shopat a potential store location if a store is opened at that location,among other attributes of consumers. Additionally, store siting may beperformed based on locations of other stores for organizations thatconsumers regularly visit and/or regularly visit in connection withvisits to existing stores for the business, or on areas that consumersregularly visit. Based on these characteristics of consumers, aprediction may be made that consumers would shop at a store in aproposed location, which may be described in any suitable termsincluding numbers of customers or numbers of sales. Thus, answers tothese questions may be obtained from a consumer analytics platform asdescribed above and used to perform store siting choices.

Similarly, questions regarding advertising effectiveness may be answeredusing the consumer analytics platform as described above. For example,consumers who have passed by the advertisement, and therefore likelyviewed an advertisement, can be identified. Information about consumerswho passed by an advertisement can then be queried to determine if theconsumers subsequently went to an advertised business or to a businessthat sells an advertised product. Additionally, for consumers who didvisit a business, a determination can be made about whether thisdeviated from normal behavior for a consumer. If consumers visitedbusinesses associated with an advertisement, then the advertisement mayhave been effective, particularly if visiting that business deviatedfrom the consumer's normal behavior. Additionally, if the advertisementwas determined to be effective for some consumers, predictions can bemade about whether the advertisement may be effective for otherconsumers by identifying other consumers with at least some similarcharacteristics.

As another example, a study can be performed to determine competitors ofa particular business. To do so, characteristics of consumers that visitthe particular business can be determined, including behaviors in whichthe consumers are engaging when they visit those businesses. Thesecharacteristics can then be compared to characteristics for otherconsumers that do not visit the particular business but share many ofthe same characteristics. This can be done to identify a group ofconsumers sharing many characteristics, but that visit either theparticular visit or visit other businesses. The shared characteristicsmay include shared identities and preferences as well as sharedbehaviors. Once these other consumers that share characteristics butvisit other businesses have been identified, the other businessesvisited by the other consumers may be inferred by the consumer analyticsplatform to be competitors of the particular business, based on theseshared characteristics.

As discussed above in connection with FIG. 9, while not illustrated inthe example of FIG. 10, as discussed above in some cases inferencesand/or predictions for a study may be determined based on purchase dataand/or demographic data, in addition to location data. Embodiments thatreview purchase data and/or demographic data to make inferences and/orpredictions may do so in any suitable manner, including as in theexamples described above.

Techniques operating according to principles described herein may beimplemented in any suitable manner. For example, the methods and systemsdescribed herein may be deployed in part or in whole through a machinethat executes computer software, program codes, and/or instructions on aprocessor. The processor may be part of a server, client, networkinfrastructure, mobile computing platform, stationary computingplatform, or other computing platform. A processor may be any kind ofcomputational or processing device capable of executing programinstructions, codes, binary instructions and the like. The processor maybe or include a signal processor, digital processor, embedded processor,microprocessor or any variant such as a co-processor (math co-processor,graphic co-processor, communication co-processor and the like) and thelike that may directly or indirectly facilitate execution of programcode or program instructions stored thereon. In addition, the processormay enable execution of multiple programs, threads, and codes. Thethreads may be executed simultaneously to enhance the performance of theprocessor and to facilitate simultaneous operations of the application.By way of implementation, methods, program codes, program instructionsand the like described herein may be implemented in one or more threads.The threads may spawn other threads that may have assigned prioritiesassociated with them; the processor may execute these threads based onpriority or any other order based on instructions provided in theprogram code. The processor may include memory that stores methods,codes, instructions and programs as described herein and elsewhere. Theprocessor may access a storage medium through an interface that maystore methods, codes, and instructions as described herein andelsewhere. The storage medium associated with the processor for storingmethods, programs, codes, program instructions or other type ofinstructions capable of being executed by the computing or processingdevice may include but may not be limited to one or more of a CD-ROM,DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.“Storage medium,” as used herein, refers to tangible storage media.Tangible storage media are non-transitory and have at least onephysical, structural component. In a storage medium, at least onephysical, structural component has at least one physical property thatmay be altered in some way during a process of creating the medium withembedded information, a process of recording information thereon, or anyother process of encoding the medium with information. For example, amagnetization state of a portion of a physical structure of acomputer-readable medium may be altered during a recording process.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server and other variants such as secondaryserver, host server, distributed server and the like. The server mayinclude one or more of memories, processors, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, any of the devices attached to the serverthrough an interface may include at least one storage medium capable ofstoring methods, programs, code and/or instructions. A centralrepository may provide program instructions to be executed on differentdevices. In this implementation, the remote repository may act as astorage medium for program code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, interne client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherclients, servers, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs or codes as described hereinand elsewhere may be executed by the client. In addition, other devicesrequired for execution of methods as described in this application maybe considered as a part of the infrastructure associated with theclient.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements.

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a network carrying out a protocol for Global System for MobileCommunications (GSM), General Packet Radio Service (GPRS), anythird-generation (3G) network, Evolution-Data Optimized (EVDO), ad hocmesh, Long-Term Evolution (LTE), Worldwide Interoperability forMicrowave Access (WiMAX), or other network types.

The methods, programs codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on a peer topeer network, mesh network, or other communications network. The programcode may be stored on the storage medium associated with the server andexecuted by a computing device embedded within the server. The basestation may include a computing device and a storage medium. The storagedevice may store program codes and instructions executed by thecomputing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable storage media that may include:computer components, devices, and recording media that retain digitaldata used for computing for some interval of time; semiconductor storageknown as random access memory (RAM); mass storage typically for morepermanent storage, such as optical discs, forms of magnetic storage likehard disks, tapes, drums, cards and other types; processor registers,cache memory, volatile memory, non-volatile memory; optical storage suchas CD, DVD; removable media such as flash memory (e.g. USB sticks orkeys), floppy disks, magnetic tape, paper tape, punch cards, standaloneRAM disks, Zip drives, removable mass storage, off-line, and the like;other computer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipments, servers, routers and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps thereof, may berealized in hardware, software or any combination of hardware andsoftware suitable for a particular application. The hardware may includea general purpose computer and/or dedicated computing device or specificcomputing device or particular aspect or component of a specificcomputing device. The processes may be realized in one or moremicroprocessors, microcontrollers, embedded microcontrollers,programmable digital signal processors or other programmable device,along with internal and/or external memory. The processes may also, orinstead, be embodied in an application specific integrated circuit, aprogrammable gate array, programmable array logic, or any other deviceor combination of devices that may be configured to process electronicsignals. It will further be appreciated that one or more of theprocesses may be realized as a computer executable code capable of beingexecuted on a machine readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

FIG. 11 illustrates one exemplary implementation of a computing devicein the form of a computing device 1100 that may be used in a systemimplementing the techniques described herein, although others arepossible. It should be appreciated that FIG. 11 is intended neither tobe a depiction of necessary components for a computing device to operatein accordance with the principles described herein, nor a comprehensivedepiction.

Computing device 1100 may comprise at least one processor 1102, anetwork adapter 1104, and computer-readable storage media 1106.Computing device 1100 may be, for example, a desktop or laptop personalcomputer, a server, a collection of personal computers or servers thatoperate together, or any other suitable computing device. Networkadapter 1104 may be any suitable hardware and/or software to enable thecomputing device 1100 to communicate wired and/or wirelessly with anyother suitable computing device over any suitable computing network. Thecomputing network may include wireless access points, switches, routers,gateways, and/or other networking equipment as well as any suitablewired and/or wireless communication medium or media for exchanging databetween two or more computers, including the Internet. Computer-readablemedia 1106 may be adapted to store data to be processed and/orinstructions to be executed by processor 1102. Processor 1102 enablesprocessing of data and execution of instructions. The data andinstructions may be stored on the computer-readable storage media 1106and may, for example, enable communication between components of thecomputing device 1100.

The data and instructions stored on computer-readable storage media 1106may comprise computer-executable instructions implementing techniqueswhich operate according to the principles described herein. In theexample of FIG. 11, computer-readable storage media 1106 storescomputer-executable instructions implementing various facilities andstoring various information as described above. Computer-readablestorage media 1106 may store a consumer analytics facility 1108 forobtaining location data for consumers via network adapter 1104 anddetermining characteristics of the consumers. Consumer analyticsfacility 1108 may perform any of the exemplary techniques describedabove, and may include any of the exemplary facilities described above.Computer-readable storage media 1106 may also include data sets to beused by the consumer analytics facility 1108, including a data set 1110of consumer characteristics, which could include profiles for consumers,and a data set 1112 of points of interests, which could includeinformation about locations and types of points of interest.

While not illustrated in FIG. 11, a computing device may additionallyhave one or more components and peripherals, including input and outputdevices. These devices can be used, among other things, to present auser interface. Examples of output devices that can be used to provide auser interface include printers or display screens for visualpresentation of output and speakers or other sound generating devicesfor audible presentation of output. Examples of input devices that canbe used for a user interface include keyboards, and pointing devices,such as mice, touch pads, and digitizing tablets. As another example, acomputing device may receive input information through speechrecognition or in other audible format.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

All documents referenced herein are hereby incorporated by reference.

1.-18. (canceled)
 19. A method comprising: operating at least one programmed processor to carry out a set of acts, the at least one programmed processor being programmed with executable instructions identifying the set of acts, the set of acts comprising: for each consumer of a plurality of consumers, obtaining location data for a current location of the consumer and comparing the location data to at least one location for at least one known setting to determine a setting corresponding to the location data; and inferring, for an organization associated with a setting identified as corresponding to the location data, competitors of the organization based at least in part on the location data.
 20. The method of claim 19, wherein: the set of acts further comprises determining at least one characteristic of a first set of consumers who visit the organization; determining, for a second set of consumers sharing the at least one characteristic, at least one second organization visited by the second set of consumers, the at least one second organization being of a same type as the organization; and the inferring comprises identifying the at least one second organization as a competitor based at least in part on the second set of consumers visits to the at least one second organization.
 21. The method of claim 20, wherein determining the at least one characteristic of the first set of consumers comprises determining a first pattern in behavior of the first set of consumers in visiting the organization; and wherein determining the at least one second organization comprises determining at least one second pattern in behavior of the second set of consumers in visiting the at least one second organization, the at least one second pattern being similar to the first pattern.
 22. The method of claim 21, wherein determining the first pattern comprises determining a set of other organizations visited in close proximity in time with the organization by the first set of consumers and determining the at least one second pattern comprises identifying at least one second organization that the second set of consumers visits in close proximity in time to the set of other organizations. 